Anti-falling safety device detection system for construction hoist

Abstract

The invention relates to an anti-falling safety device detection system for a construction hoist and an anti-falling safety device comprising or using the anti-falling safety device detection system. The anti-falling safety device detection system comprises a sensor group which comprises an encoder, a Hall sensor, a strain gauge and an acceleration sensor; the data acquisition unit is used for acquiring and preprocessing data acquired by the sensor group; the data processing unit is configured to determine the running speed, the braking operation speed parameter, the gear shaft load and the braking distance of the anti-falling safety device based on the data acquired and preprocessed by the data acquisition unit; and the detection result determination unit is configured to compare the determined running speed of the anti-falling safety device, the braking operation speed parameter, the gear shaft load and the braking distance with corresponding preset threshold ranges, and determine a detection result based on a comparison result.

Description

Technical Field

[0001] This disclosure relates to the field of testing, and in particular to a testing system for fall arresters used in construction hoists and fall arresters that include or use the same. Background Technology

[0002] Construction hoist fall arrestors are critical safety components in construction machinery, and their reliability is paramount. To ensure their effectiveness, regular testing is required according to relevant standards. Improvements may be needed in enhancing the convenience and accuracy of the testing process, such as in efficiently and collaboratively acquiring multi-dimensional operating parameters and ensuring their precise temporal correspondence to support comprehensive analysis and result determination. Furthermore, the testing process's reliance on on-site operations, its safety, and its overall efficiency also require further improvement.

[0003] It should be noted that the content described herein is only to provide background information in relation to this disclosure and is not necessarily prior art. Summary of the Invention

[0004] In view of the above problems, this disclosure aims to provide a fall arrestor detection system for construction hoists and a fall arrestor that includes or uses the same.

[0005] The first aspect of this disclosure, a fall arrestor detection system for a construction hoist, includes: a sensor group arranged on the fall arrestor, the sensor group including an encoder for acquiring gear shaft rotation data of the fall arrestor, a Hall sensor for acquiring braking data of the fall arrestor, a strain gauge for acquiring gear shaft load data of the fall arrestor, and an accelerometer for acquiring acceleration data of the fall arrestor; a data acquisition unit for acquiring and preprocessing the data acquired by the sensor group; a data processing unit configured to determine the operating speed, braking speed parameters, gear shaft load, and braking distance of the fall arrestor based on the data acquired and preprocessed by the data acquisition unit; and a detection result determination unit configured to compare the determined operating speed, braking speed parameters, gear shaft load, and braking distance of the fall arrestor with corresponding preset threshold ranges, and determine the detection result based on the comparison result.

[0006] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the data acquisition unit further includes a synchronous acquisition subunit, which is configured to synchronously acquire data obtained by each of the sensor groups at a preset heterogeneous sampling frequency in response to a fall trigger signal, and to perform timestamp alignment on each of the acquired data, wherein the data processing unit is further configured to determine the operating speed, braking operation speed parameters, gear shaft load and braking distance of the fall arrestor based on the timestamp aligned data.

[0007] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the fall arrestor detection system is further configured to: adjust the sampling frequency of the acceleration sensor based on the speed value fed back by the encoder in real time.

[0008] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the data processing unit and the detection result determination unit are located on a cloud server. The fall arrestor detection system further includes a communication unit connected to the data acquisition unit and configured to transmit the acquired and preprocessed data wirelessly to the cloud server via a 5G cellular network or Wi-Fi.

[0009] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the detection result determination unit determines that the fall arrestor is faulty based on the fact that at least one of the fall arrestor's operating speed, braking operation speed parameters, gear shaft load, and braking distance does not conform to the corresponding preset threshold range.

[0010] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the data processing unit is configured to use the fall start point and the point of complete rest identified by the acceleration sensor as the integration interval when determining the braking distance, and to perform zero-bias compensation calibration on the acceleration data using the rate of change of speed fed back by the encoder.

[0011] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, an encoder is arranged at the tail of the fall arrestor and is connected to the gear shaft of the fall arrestor via a drive, and an acceleration sensor is arranged on the surface of the middle part of the outer shell of the fall arrestor.

[0012] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the data acquisition unit further includes a signal filtering subunit, which is configured to identify and filter out periodic vibration noise generated by the meshing of the elevator guide rail rack based on the signal from the acceleration sensor.

[0013] According to one or more embodiments of the fall arrestor detection system of this disclosure, optionally, the signal filtering subunit employs an adaptive filter whose filtering parameters can be dynamically adjusted according to the real-time operating speed determined by the encoder signal.

[0014] According to one or more embodiments of the fall arrestor detection system of the present disclosure, optionally, the detection result determination unit is further configured to: determine the dynamic braking force in association with the gear shaft load and the real-time speed obtained by integrating the signal from the acceleration sensor, and compare it with a preset braking force curve.

[0015] According to one or more embodiments of the fall arrestor detection system disclosed herein, optionally, the sensor group further includes a sound sensor for obtaining sound data; the detection result determination unit is further configured to perform spectrum analysis on the sound data to extract abnormal high-frequency noise information, and fuse the abnormal high-frequency noise information with the dynamic braking force to jointly determine whether there is uneven wear on the fall arrestor.

[0016] According to one or more embodiments of the fall arrestor detection system of this disclosure, optionally, the detection result determination unit is configured to perform time correlation calculation between the energy level of abnormal high-frequency noise information and the fluctuation amplitude of the dynamic braking force curve, and determine the uneven wear of the fall arrestor based on the discrepancy between the calculation result and a preset correlation threshold.

[0017] According to one or more embodiments of the fall arrestor detection system disclosed herein, the fall arrestor detection system may optionally further include a report generation unit, which is configured to automatically generate a detection report based on the detection results of the detection result determination unit.

[0018] Optionally, the fall arrestor detection system according to one or more embodiments of the present disclosure may further include a mobile terminal interaction unit, which is configured to bind device information, issue detection tasks, and view results through the identification code of the fall arrestor.

[0019] According to one or more embodiments of the fall arrestor detection system of the present disclosure, optionally, the data acquisition unit further includes a temperature compensation subunit, which is configured to adaptively correct the gear shaft load calculated from the strain gauge signal based on the ambient temperature.

[0020] The second aspect of this disclosure includes a fall arrestor that includes or uses a fall arrestor detection system according to any of the foregoing embodiments. Attached Figure Description

[0021] Figure 1 A schematic diagram of a fall arrestor detection system 100 for a construction hoist according to some embodiments is shown.

[0022] Figure 2 A structural schematic diagram of a fall arrestor 200 according to some embodiments is shown. Detailed Implementation

[0023] The following description describes some of the various embodiments of this disclosure, intended to provide a basic understanding of the disclosure. It is not intended to identify key or decisive elements of the disclosure or to limit the scope of protection sought.

[0024] For purposes of brevity and illustrative purposes, the principles of this disclosure are described herein primarily with reference to exemplary embodiments thereof. However, those skilled in the art will readily recognize that the same principles are equivalently applicable to all types of fall arrestor detection systems for construction hoists and fall arrestors that include or use thereof, and that these same principles may be implemented therein, and that any such variations do not depart from the true spirit and scope of this patent application.

[0025] Furthermore, reference is made in the accompanying drawings, which illustrate specific exemplary embodiments. Electrical, mechanical, logical, and structural changes may be made to these embodiments without departing from the spirit and scope of this disclosure. Moreover, while features of this disclosure are disclosed in combination with only one of several embodiments, such features may be combined with one or more other features of other embodiments if desired and / or advantageous for any given or identifiable function. Therefore, the following description should not be considered limiting in any sense, and the scope of this disclosure is defined by the appended claims and their equivalents.

[0026] Terms such as “possessing” and “comprising” indicate that, in addition to having the units (modules) and steps that are directly and explicitly stated in the specification and claims, the technical solutions of this disclosure do not exclude the presence of other units (modules) and steps that are not directly or explicitly stated.

[0027] In this document, the term "lift" primarily refers to a fixed or mobile construction machine used for the vertical transportation of personnel or goods, typically consisting of a guide rail frame, a cage, a drive unit, and safety protection devices. In this context, the term specifically refers to a "construction lift," whose core characteristic is its temporary installation and use in construction sites to solve the problem of vertical transportation of personnel and materials in high-rise buildings. Its scope is not limited to specific drive methods (such as rack and pinion or wire rope) or structural forms (such as single-cage or double-cage), but encompasses all vertical lifting equipment used in the construction field that requires fall arresters as a critical safety guarantee. In this document, the term "fall arrester" refers to a safety device that automatically triggers braking when the lift's operating speed exceeds a preset limit (i.e., a "fall"), stopping the cage or counterweight on the guide rail frame or safely decelerating it to a stop.

[0028] In this paper, the term "braking distance" is defined as the linear displacement traversed from the moment the braking action of the fall arrestor is triggered until the elevator cage or counterweight comes to a complete stop relative to the guide rail. The term "dynamic braking force" characterizes the time-varying load force borne by key transmission components such as gear shafts during the braking process of the fall arrestor, reflecting the dynamic changes in force during the braking event. The term "uneven wear" is used to describe the non-uniform wear of the surface material of the braking components (such as brake gears and friction pads) or related transmission components (such as gear shafts) of the fall arrestor due to abnormal operating conditions (such as off-center loading, eccentricity, or localized abnormal friction).

[0029] In the field of construction hoist safety inspection, the traditional method of periodically inspecting fall arrestor safety devices has long relied on disassembling the equipment from the hoist and sending it to a professional organization for offline inspection. This model not only results in long downtime and high economic costs, but the disassembly and reassembly process itself may also introduce new safety hazards. The inventors recognized that the problem lies in the fact that traditional methods cannot continuously and online monitor and evaluate the performance of safety devices under actual working conditions. Based on this insight, the inventors proposed to "embed" the inspection capability into the safety device.

[0030] However, realizing this concept faces multiple technical challenges in the special scenario of a heavy elevator falling. First, it is necessary to reasonably deploy multiple sensors in a limited space and ensure the accuracy and synchronization of their data collection, which places certain requirements on the selection of sensors. Second, it is necessary to achieve reliable data transmission and processing in a complex engineering site environment with strong vibration and multiple electromagnetic interferences. Finally, it is necessary to conduct more accurate correlation analysis between the multi-source heterogeneous sensor data and the performance parameters specified by national standards.

[0031] Figure 1 A schematic diagram of a fall arrestor detection system 100 for a construction hoist according to some embodiments is shown. The fall arrestor detection system 100 may include a sensor group 110 (showing multiple different types of sensors, such as encoder 111, Hall sensor 112, strain gauge 113 and acceleration sensor 114), a data acquisition unit 120, a data processing unit 130, and a detection result determination unit 140. Figure 1The example shown illustrates that the data processing unit 130 and the detection result determination unit 140 are located on a cloud server. However, it is understood that the arrangement of the components 110-140 is not limited to this. For example, the data processing unit 130 and the detection result determination unit 140 may also be integrated with or arranged on the fall arrestor along with the sensor group 110 and the data acquisition unit 120, or the components 110-140 may be integrated into a detection device and coupled to the fall arrestor. As another example, the data processing unit 130 and the detection result determination unit 140 may be implemented using the processing capabilities of the fall arrestor itself, such as using the fall arrestor's processor.

[0032] The fall arrestor testing system 100 can comprehensively monitor the operational status of fall arrestors, for example, through the coordinated data acquisition of multiple dedicated sensors. This system supports on-site, non-disassembly testing; for example, by integrating sensor arrays 110 onto the fall arrestor, it avoids the traditional disassembly and testing process. The data acquisition unit 120 can perform preliminary processing on the raw sensor signals, such as filtering or amplification, to improve data usability and accuracy. The data processing unit 130 can calculate key performance parameters, such as operating speed or braking distance, based on the preprocessed data. The test result determination unit 140 can compare parameter ranges with preset standards (such as GB / T34025-2017) and output the test conclusion. The modular design of system 100 allows for flexible deployment, such as selecting local or cloud processing modes based on network conditions or computing needs. Overall, system 100 improves the flexibility and adaptability of the testing process, supports various deployment scenarios, and significantly enhances testing accuracy.

[0033] Sensor group 110 is arranged on the fall arrestor. Sensor group 110 includes an encoder 111 for acquiring gear shaft rotation data, a Hall sensor 112 for acquiring braking data, a strain gauge 113 for acquiring gear shaft load data, and an accelerometer 114 for acquiring acceleration data. Sensor group 110 can, for example, cover key operating parameters of the fall arrestor through a combination of multiple sensor types. Encoder 111 can be used to monitor the number of gear shaft rotations or frequency to estimate operating speed. Hall sensor 112 can be arranged near the braking mechanism to capture speed change curves during braking. Strain gauge 113 can be attached to the stressed area of ​​the gear shaft to convert deformation into electrical signals to reflect load magnitude. Accelerometer 114 can, for example, employ a triaxial design and be mounted on the housing surface to record acceleration changes and calculate braking distance. Each sensor can operate independently but provides complementary data through collaborative acquisition; for example, encoder 111 provides a speed reference, while accelerometer 114 provides motion state verification. In this way, multi-dimensional data acquisition can be achieved through specific types of sensor groups, which facilitates subsequent comprehensive performance testing.

[0034] The data acquisition unit 120 can acquire and preprocess the data obtained by the sensor array. The data acquisition unit 120 may, for example, have multi-channel input capability, simultaneously receiving analog or digital signals output from each sensor. This unit may include, for example, signal conditioning circuitry to filter, amplify, or perform analog-to-digital conversion on the raw signals to reduce noise interference. Preprocessing may include data compression or formatting to facilitate transmission or storage. For example, for the weak signal output by strain gauge 113, an amplification circuit can be used to improve the signal-to-noise ratio; for the data from accelerometer 114, a low-pass filter can be applied to eliminate high-frequency vibrations. The data acquisition unit 120 can also temporarily buffer data to cope with network transmission delays. This further improves data quality and the accuracy and reliability of the detection system 100, reducing the risk of misjudgment.

[0035] The data processing unit 130 can be configured to determine the operating speed, braking speed parameters, gear shaft load, and braking distance of the fall arrestor based on data acquired and preprocessed by the data acquisition unit. The data processing unit 130 can, for example, analyze and calculate the preprocessed data using an algorithm module. The operating speed can be calculated based on the pulse signal from the encoder 111 and gear shaft parameters (such as module and number of teeth). The braking speed parameters can be extracted, for example, from data from the Hall sensor 112, such as the initial braking speed or average speed. The gear shaft load can be calculated using the signal from the strain gauge 113 and material mechanics formulas. The braking distance can be obtained by numerically integrating the data from the acceleration sensor 114. The data processing unit 130 can also perform data fusion, such as correlating speed and acceleration data to verify consistency, which enables accurate calculation of key parameters.

[0036] The test result determination unit 140 can be configured to compare the determined operating speed, braking speed parameters, gear shaft load, and braking distance of the fall arrestor with corresponding preset threshold ranges, and determine the test result based on the comparison results. The test result determination unit 140 can, for example, have a built-in threshold database storing acceptable ranges based on national standards (such as GB / T34025-2017). The comparison process can be performed item by item; for example, first determining whether the operating speed exceeds the limit, and then checking whether the braking distance meets the standard. The judgment logic can include Boolean operations; if all parameters are qualified, "pass" is output; otherwise, unqualified items are marked. For boundary cases, unit 140 can introduce a tolerance mechanism, such as allowing slight deviations. The test result can be accompanied by a confidence index to reflect data quality. Unit 140 can also record historical comparison results for trend analysis. In this way, automated and standardized test judgment can be achieved, improving the efficiency and consistency of the test.

[0037] The Fall Arrestor Safety Device Testing System 100 integrates multiple sensors and a data processing chain to achieve on-site, non-disassembly testing of fall arrestors for construction hoists. This system can replace the traditional disassembly and inspection method, reducing equipment downtime, for example, from several days to several hours. The testing process is based on objective data, reducing the risk of human error and, for example, supporting electronic recording for easy traceability. The modular design of System 100 allows for configuration adjustments based on actual needs, such as local processing in situations with poor network conditions. Overall, System 100 improves testing efficiency and safety while reducing maintenance costs, providing a highly efficient and low-cost solution for testing critical components of large hoists.

[0038] In some embodiments, the data acquisition unit 120 further includes a synchronous acquisition subunit 121, configured to synchronously acquire data from each sensor group at a preset heterogeneous sampling frequency in response to a fall trigger signal, and to perform timestamp alignment on each acquired data. The data processing unit 130 may be further configured to determine the operating speed, braking speed parameters, gear shaft load, and braking distance of the fall arrestor based on the timestamp-aligned data. However, in other examples, the synchronous acquisition subunit 121 may not respond to a fall trigger signal, but may perform normalized acquisition synchronization and timestamp alignment operations even without a fall trigger signal, which facilitates data acquisition synchronization and alignment during normalized real-time detection.

[0039] The synchronous acquisition subunit 121 can receive a trigger signal (such as a user command or sensor threshold) at the start of detection, and then acquire data from each sensor at different frequencies. For example, the encoder 111 uses a 10ms sampling interval, while the accelerometer 114 uses a 5ms interval. Heterogeneous sampling can optimize resource utilization by setting frequencies for different parameter characteristics. Timestamp alignment can be achieved, for example, through hardware clocking or software interpolation, to ensure the consistency of the data time series. For example, subunit 121 can add a precise timestamp to each data point and perform resampling or alignment operations in subsequent processing. This synchronization mechanism can reduce analysis errors caused by acquisition delays, improve the temporal consistency of multi-source data, and further ensure the accuracy of subsequent analysis and judgment. In particular, timestamp alignment can solve the problem of sensor response delay differences. For example, at the moment of braking, the data from each sensor may not arrive simultaneously. After alignment, the event sequence can be accurately reflected, thereby improving the reliability of fault diagnosis.

[0040] The fall arrestor detection system 100 can also be configured to adjust the sampling frequency of the accelerometer 114 based on the real-time speed feedback from the encoder 111. For example, when the encoder 111 detects that the speed exceeds a calibrated value, the fall arrestor detection system 100 (e.g., its synchronous acquisition subunit 121) can control the dynamic increase of the sampling frequency of the accelerometer 114 to capture more detailed motion changes. This adjustment can be based on preset rules, such as increasing the sampling frequency accordingly for every certain percentage increase in speed. The adjustment mechanism can avoid oversampling or undersampling of data, optimizing storage and transmission efficiency. For example, a lower frequency can be used during low-speed phases, while a higher frequency can be used during braking. In this way, adaptive data acquisition can be achieved, improving data validity and subsequent analysis accuracy, while reducing resource waste. Especially in scenarios where the braking process of the fall arrestor is short and dynamically changing, fixed sampling may lose key details, while real-time adjustment based on speed can ensure the capture of braking peaks, thereby improving detection accuracy.

[0041] In some embodiments, the data processing unit 130 and the detection result determination unit 140 are located on a cloud server. The fall arrestor detection system also includes a communication unit (not shown), which is connected to the data acquisition unit 120 and configured to wirelessly transmit the acquired and preprocessed data to the cloud server via a 5G cellular network or Wi-Fi. Specifically, the communication unit may support various wireless protocols, such as 5G or Wi-Fi, or other wireless transmission methods, such as LTE, Bluetooth, etc., to adapt to different field environments. Data transmission may include encryption and compression to ensure security and efficiency. Cloud deployment allows for elastic allocation of computing resources, such as automatic scaling when processing large-scale data, to achieve remote data processing, support real-time detection, and centralized management. Furthermore, placing the processing unit in the cloud facilitates real-time detection and data analysis, allowing results to be obtained without disassembling the equipment, while cloud storage facilitates historical data management and multi-device monitoring.

[0042] In some embodiments, the detection result determination unit 140 may determine that the fall arrestor is faulty based on at least one of the following parameters—operating speed, braking speed, gear shaft load, and braking distance—not conforming to a corresponding preset threshold range. The detection result determination unit 140 may employ flexible judgment strategies, such as triggering a fault flag if a single parameter exceeds the limit, or using a weighted comprehensive evaluation. The threshold range may be dynamically updated based on national standards or historical data. For discrepancies, unit 140 may identify the specific parameter and the degree of deviation for subsequent maintenance guidance. This provides a relatively flexible fault identification mechanism to adapt to different detection needs.

[0043] In some embodiments, the data processing unit 130 can be configured to use the fall start point and the point of complete stillness identified by the acceleration sensor as the integration interval when determining the braking distance, and to perform zero-bias compensation calibration on the acceleration data using the rate of change of velocity fed back by the encoder 111. That is, the data processing unit 130 can first identify the fall start and end points using the acceleration sensor 114, for example, based on an acceleration abrupt change threshold. After the integration interval is determined, numerical integration is used to calculate the displacement. Zero-bias compensation can use the velocity data of the encoder 111 as a reference, for example, by comparing the integrated acceleration velocity with the encoder velocity to correct sensor deviation. This calibration can be performed periodically or based on event triggering, which can improve the accuracy of braking distance calculation and reduce the impact of sensor errors.

[0044] In some embodiments, the encoder 111 can be arranged at the tail of the fall arrestor and driven through the gear shaft of the fall arrestor, while the accelerometer 114 can be arranged on the surface of the middle portion of the fall arrestor's housing. The tail-end arrangement of the encoder 111 facilitates direct monitoring of the gear shaft rotation, reducing transmission errors. The surface mounting of the accelerometer 114 ensures synchronization with the housing movement, avoiding interference from internal structures. This arrangement considers spatial constraints and measurement effectiveness; for example, the tail-end facilitates mechanical connection, and the middle surface reduces vibration attenuation, further improving data acquisition accuracy by optimizing sensor placement. Specifically, the tail-end arrangement of the encoder directly captures gear shaft movement, avoiding indirect measurement delays; the middle surface mounting of the accelerometer reflects overall acceleration, reducing local vibration noise. This combined arrangement improves the reliability of the acquired data.

[0045] In some embodiments, the data acquisition unit 120 further includes a signal filtering subunit 122, which can be configured to identify and filter out periodic vibration noise generated by the rack meshing of the elevator guide frame based on the signal from the acceleration sensor 114. The signal filtering subunit 122 can, for example, employ a digital filter (such as a band-stop filter) to filter out noise at the rack meshing frequency. The meshing frequency can be calculated based on the speed of the encoder 111 and the rack parameters. The filtering parameters can be preset or adaptively adjusted to match actual operating conditions. The retained acceleration data after filtering is more focused on the fall event. In scenarios particularly involving heavy machinery (e.g., elevators) falling, rack meshing noise is a common disturbance during elevator operation; filtering it allows for more accurate identification of fall-related accelerations, improving the reliability of braking distance calculations.

[0046] The signal filtering subunit 122 can further employ an adaptive filter, whose filtering parameters can be dynamically adjusted according to the real-time operating speed determined by the encoder signal. The adaptive filter can, for example, be based on the LMS algorithm, updating the filter coefficients according to real-time speed changes. Speed ​​changes affect the rack meshing frequency, and dynamic adjustment can continuously optimize the filtering effect. For example, when the speed increases, the filter automatically adjusts the stopband frequency. This enables adaptive noise filtering and improves robustness under different operating conditions.

[0047] In some embodiments, the detection result determination unit 140 can also be configured to: determine the dynamic braking force by correlating the gear shaft load with the real-time velocity obtained by integrating the signal from the acceleration sensor 114, and compare it with a preset braking force curve. The detection result determination unit 140 can first calculate the gear shaft load from the strain gauge 113 data, and simultaneously obtain the velocity curve by integrating the acceleration data. The dynamic braking force can be derived from the relationship between load and acceleration. The preset braking force curve can be derived from national standards or historical qualified data. The comparison can include shape similarity or peak deviation analysis. In the scenario of elevator fall prevention detection, this correlation analysis can reveal the mechanical performance during braking; for example, braking force fluctuations may indicate wear, while simple parameter threshold comparisons may miss such issues.

[0048] For example, sensor group 110 may further include an acoustic sensor 115 for acquiring sound data; the detection result determination unit 140 is also configured to perform spectral analysis on the sound data to extract abnormal high-frequency noise information, and fuse the abnormal high-frequency noise information with dynamic braking force to jointly determine whether uneven wear exists in the fall arrestor. The acoustic sensor 115 may be installed near the housing to collect operating sounds. Spectral analysis can identify specific high-frequency components, such as friction noise. Fusion may employ weighted or logical rules, for example, triggering a wear alarm when high-frequency noise and braking force fluctuations occur simultaneously. This increases the dimensions of wear detection and improves diagnostic comprehensiveness because the fusion of sound and dynamic data can cross-verify wear conditions; for example, high-frequency noise represents surface roughness, while braking force fluctuations represent changes in mechanical properties, and the combination can further reduce the probability of false alarms.

[0049] For example, the detection result determination unit 140 can also be configured to determine uneven wear of the fall arrestor by performing a time correlation calculation between the energy level of abnormal high-frequency noise information and the fluctuation amplitude of the dynamic braking force curve, and by determining whether the calculation result matches a preset correlation threshold. The time correlation calculation can, for example, employ a cross-correlation algorithm to analyze the temporal synchronicity of noise energy and braking force fluctuations. A high correlation may indicate that wear is concentrated at a certain point. The preset threshold can be set based on experimental data. This quantifies the wear and can support predictive maintenance of the subsequent detection system.

[0050] In some embodiments, the fall arrestor testing system further includes a report generation unit configured to automatically generate a test report based on the test results determined by the test result determination unit. The report generation unit can, for example, integrate equipment information, test data, judgment results, and timestamps to output a standardized report (such as in PDF format). The report may include charts and conclusions, and supports export or printing, thereby achieving traceable documentation of the test results for easy management. For example, the test report includes basic equipment information (product number, model, installation time, etc.), test environment information (test date, location, elevator parameters, etc.), raw data collected by multiple sensors, data processing results, national standard thresholds for various parameters, and judgment conclusions. The report supports PDF export, and users can query, download, and print it via mobile devices or computers, achieving traceability and archiving of the test results.

[0051] In some embodiments, the fall arrestor testing system further includes a mobile interaction unit, configured to bind device information, issue testing tasks, and view results via the fall arrestor's identification code. The mobile interaction unit can be deployed on a smartphone or tablet, identifying the device identification code by scanning a code (such as a QR code). After binding, the user can submit testing tasks and view real-time progress. The module can support offline caching or online synchronization. This provides a convenient human-machine interface, improving the ease of management for maintenance personnel during the testing process. The devices on which the mobile interaction unit can be deployed can include common smart devices such as iOS or Android phones, tablets, laptops, personal computers, dedicated testing equipment, etc.

[0052] In some embodiments, the data acquisition unit 120 further includes a temperature compensation subunit 123, which can be configured to adaptively correct the gear shaft load calculated from the strain gauge signal based on the ambient temperature. The temperature compensation subunit 123 can access temperature sensor data, such as data based on thermocouples or integrated sensors. The correction algorithm can employ a linear or polynomial model to compensate for strain gauge drift caused by temperature. For example, when the temperature rises, the calculated load value is automatically reduced. This further reduces the impact of temperature on load measurement and improves data accuracy. Especially in heavy-duty elevator fall scenarios, where the temperature of related equipment changes rapidly and drastically, this compensation can avoid misjudgments caused by thermal expansion, making it particularly suitable for long-term monitoring scenarios.

[0053] Furthermore, the detection system 100 of any of the aforementioned embodiments can be further optimized to improve the efficiency and accuracy of detecting, judging, and resolving the problems. For example, in some embodiments, the detection system 100 can adopt a multi-source fault information acquisition mode that combines sensor data and user semantic description. This mode involves: on the one hand, actively collecting and uploading objective operating parameters (such as vibration amplitude, frequency, sound spectrum, etc.) through the sensor group built into the fall arrestor (such as the aforementioned four types of sensors, or triaxial vibration sensors, sound sensors, etc.); on the other hand, maintenance personnel can provide an interactive interface to users on a mini-program, allowing them to supplement the description of the fault phenomenon in natural language (such as "abnormal noise during operation"). The data processing unit and the detection result determination unit can integrate objective quantitative data with subjective semantic descriptions to form a structured and complete set of fault information. This allows fault diagnosis to include both machine-readable precise data and human experiential descriptions, significantly improving the subsequent diagnostic engine's ability to identify complex, non-standard faults and its accuracy, overcoming the potential bias of relying solely on sensor data or user descriptions.

[0054] Corresponding to the above embodiments, the data processing unit and / or detection result determination unit of system 100 can further be based on a knowledge base and rule engine. For example, firstly, the integrated fault information is automatically compared with common fault feature models pre-stored in the knowledge base; if a match is successful, a standardized solution is automatically retrieved from the knowledge base and pushed to the user (maintenance personnel) to achieve immediate, self-service troubleshooting. If no known type is matched or the confidence level is insufficient, the work order is automatically routed to the human service module, along with the complete data package collected earlier, so that after-sales experts can intervene through video calls, remote guidance, etc. This achieves efficient resource scheduling, allowing common and simple problems to be handled automatically by the system, thereby concentrating human expert resources on solving truly difficult and complex problems, significantly shortening the average fault resolution time overall, and optimizing the manpower allocation efficiency of the service team.

[0055] This disclosure discloses another aspect of a fall arrestor, which may include the detection system 100 according to any of the foregoing embodiments. Alternatively, the fall arrestor may use the detection system 100 according to any of the foregoing embodiments, and thus, the relevant components of the fall arrestor may be adaptively coupled to the detection system 100.

[0056] For example, Figure 2A schematic diagram of a fall arrestor 200 according to some embodiments is shown. The arrangement of four types of sensors selected in this solution is illustrated by way of example: an encoder 210 is located at the rear of the fall arrestor and connected to the gear shaft drive; a Hall sensor 240 is located near the braking mechanism, particularly where it needs to be aligned with the movement trajectory of the braking component; a strain gauge 230 is located at a critical stress point on the gear shaft; and an acceleration sensor 220 is located in the middle of the fall arrestor housing and is mounted horizontally.

[0057] This disclosure presents a non-disassembly-based testing system for field drop tests, constructed by integrating multi-source sensors such as encoders, Hall effect sensors, strain gauges, and accelerometers. This system effectively solves the problems of cumbersome processes and long downtime caused by equipment disassembly, inspection, and reassembly in traditional testing methods, shortening the testing cycle from days to hours. Furthermore, it eliminates the secondary installation risks and corresponding labor and transportation costs that may be introduced during the disassembly process, resulting in a significant reduction in testing costs. Through 5G / Wi-Fi wireless transmission and cloud data processing, the system can complete real-time data acquisition, processing, and accurate comparison with preset threshold ranges (e.g., based on national standards), and automatically generate traceable electronic testing reports, overcoming the drawbacks of inconvenient traditional paper report management and delayed data feedback. Thus, while achieving efficient, low-cost, and highly accurate non-disassembly testing, it provides a reliable data foundation for equipment lifecycle status monitoring and predictive maintenance.

[0058] The foregoing primarily describes the fall arrestor detection system for construction hoists and the fall arrestor including or using the same. Although only some specific embodiments of this disclosure have been described, those skilled in the art should understand that this disclosure can be implemented in many other forms without departing from its spirit and scope. Therefore, the examples and embodiments shown are to be considered illustrative rather than restrictive, and this disclosure may cover various modifications and substitutions without departing from the spirit and scope of this disclosure as defined by the appended claims.

Claims

1. A fall arrestor detection system for construction hoists, characterized in that, The fall arrestor detection system includes: A sensor group, which is arranged on the fall arrestor, includes an encoder for obtaining gear shaft rotation data of the fall arrestor, a Hall sensor for obtaining braking data of the fall arrestor, a strain gauge for obtaining gear shaft load data of the fall arrestor, and an accelerometer for obtaining acceleration data of the fall arrestor. The data acquisition unit collects and preprocesses the data obtained by the sensor group; A data processing unit is configured to determine the operating speed, braking speed parameters, gear shaft load, and braking distance of the fall arrestor based on the data acquired and preprocessed by the data acquisition unit; and The detection result determination unit is configured to compare the determined operating speed, braking operation speed parameters, gear shaft load, and braking distance of the fall arrestor with the corresponding preset threshold range, and determine the detection result based on the comparison result.

2. The fall arrestor detection system according to claim 1, characterized in that, The data acquisition unit further includes a synchronous acquisition subunit, which is configured to synchronously acquire data from each sensor group at a preset heterogeneous sampling frequency in response to a fall trigger signal, and to perform timestamp alignment on each acquired data. The data processing unit is further configured to determine the operating speed, braking operation speed parameters, gear shaft load, and braking distance of the fall arrestor based on the timestamp-aligned data.

3. The fall arrestor detection system according to claim 2, characterized in that, The fall arrestor detection system is also configured to adjust the sampling frequency of the accelerometer based on the speed value fed back by the encoder in real time.

4. The fall arrestor detection system according to claim 1, characterized in that, The data processing unit and the detection result determination unit are located on a cloud server. The fall arrestor detection system also includes a communication unit, which is connected to the data acquisition unit and configured to transmit the acquired and preprocessed data to the cloud server via a 5G cellular network or Wi-Fi.

5. The fall arrestor detection system according to claim 1, characterized in that, The detection result determination unit determines that the fall arrestor is faulty if at least one of the following parameters—operating speed, braking speed, gear shaft load, and braking distance—does not match the corresponding preset threshold range: the operating speed, braking speed parameter, gear shaft load, and braking distance of the fall arrestor.

6. The fall arrestor detection system according to claim 1, characterized in that, The data processing unit is configured to use the fall start point and the point of complete stillness identified by the acceleration sensor as the integration interval when determining the braking distance, and to perform zero-bias compensation calibration on the acceleration data using the rate of change of speed fed back by the encoder.

7. The fall arrestor detection system according to claim 1, characterized in that, The encoder is located at the rear of the fall arrestor and is connected to the gear shaft of the fall arrestor via a drive. The acceleration sensor is located on the surface of the middle part of the outer shell of the fall arrestor.

8. The fall arrestor detection system according to claim 1, characterized in that, The data acquisition unit further includes a signal filtering subunit, which is configured to identify and filter out periodic vibration noise generated by the meshing of the elevator guide rail rack based on the signal from the acceleration sensor.

9. The fall arrestor detection system according to claim 8, characterized in that, The signal filtering subunit employs an adaptive filter whose filtering parameters can be dynamically adjusted according to the real-time operating speed determined by the encoder signal.

10. The fall arrestor detection system according to claim 1, characterized in that, The detection result determination unit is further configured to: determine the dynamic braking force by associating the gear shaft load with the real-time speed obtained by integrating the signal from the acceleration sensor, and compare it with a preset braking force curve.

11. The fall arrestor detection system according to claim 10, characterized in that, The sensor group also includes a sound sensor for acquiring sound data; the detection result determination unit is further configured to perform spectral analysis on the sound data to extract abnormal high-frequency noise information, and to fuse the abnormal high-frequency noise information with the dynamic braking force to jointly determine whether the fall arrestor has uneven wear.

12. The fall arrestor detection system according to claim 11, characterized in that, The detection result determination unit is configured to perform time correlation calculation between the energy level of the abnormal high-frequency noise information and the fluctuation amplitude of the dynamic braking force curve, and determine the uneven wear of the fall arrestor based on the discrepancy between the calculation result and a preset correlation threshold.

13. The fall arrestor detection system according to claim 1, characterized in that, The fall arrestor detection system also includes a report generation unit, which is configured to automatically generate a detection report based on the detection results of the detection result determination unit.

14. The fall arrestor detection system according to claim 1, characterized in that, The fall arrestor detection system also includes a mobile terminal interaction unit, which is configured to bind device information, issue detection tasks, and view results through the identification code of the fall arrestor.

15. The fall arrestor detection system according to claim 1, characterized in that, The data acquisition unit further includes a temperature compensation subunit, which is configured to adaptively correct the gear shaft load calculated from the strain gauge signal based on the ambient temperature.

16. A fall arrestor, characterized in that, The fall arrestor includes or uses a fall arrestor detection system according to any one of claims 1-15.

Images