A method and system for monitoring abnormal noise of a speed reducer

Abstract

The application discloses an abnormal sound monitoring method and system of a speed reducer, and relates to the field of transmission device fault detection. The method is applied to an abnormal sound monitoring system, and the method comprises the following steps: acquiring working condition information of the speed reducer and monitoring data of a plurality of monitoring sensors, wherein the working condition information comprises a plurality of working condition parameters; determining a cascade screening sequence of the plurality of monitoring sensors according to the working condition information; sequentially performing abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors according to the cascade screening sequence of the plurality of monitoring sensors; and if the abnormal sound analysis result of a first monitoring sensor meets the corresponding interruption condition, outputting the monitoring data of the first monitoring sensor as target monitoring data, so as to improve the positioning analysis efficiency of subsequent fault analysis, and the first monitoring sensor is any one of the plurality of monitoring sensors. By implementing the technical scheme provided in the application, the problem of low monitoring precision of the conventional speed reducer abnormal sound is solved.

Description

Technical Field

[0001] This application relates to the technical field of transmission device fault detection, specifically to a method and system for monitoring abnormal noise in a speed reducer. Background Technology

[0002] As a core component of industrial transmission systems, the health status of speed reducers directly affects the continuity of production lines and the safety of equipment. Abnormal noises are a typical precursor to early speed reducer failures. Therefore, monitoring abnormal noises in speed reducers is of great significance for achieving predictive maintenance and reducing operation and maintenance costs.

[0003] Currently, most gearbox noise monitoring still relies on a single sensor, but the noise signal is often mixed with the normal operating noise of the equipment, resulting in a high false alarm rate.

[0004] Therefore, there is an urgent need for a high-precision method for monitoring abnormal noises in speed reducers. Summary of the Invention

[0005] To address the problem of low accuracy in traditional gearbox noise monitoring, this application provides a method and system for monitoring gearbox noise.

[0006] In a first aspect, this application provides a method for monitoring abnormal noise in a speed reducer, applied in an abnormal noise monitoring system, the method comprising:

[0007] The system acquires the operating condition information of the speed reducer and monitoring data from multiple monitoring sensors, wherein the operating condition information includes various operating condition parameters;

[0008] Based on the operating condition information, determine the cascading screening order of the multiple monitoring sensors;

[0009] According to the cascaded screening order of the multiple monitoring sensors, the monitoring data of the multiple monitoring sensors are sequentially analyzed for abnormal noise and interruption conditions are determined.

[0010] If the abnormal noise analysis result of the first monitoring sensor meets its corresponding interruption condition, the monitoring data of the first monitoring sensor is output as the target monitoring data, thereby improving the location analysis efficiency of subsequent fault analysis. The first monitoring sensor can be any one of the multiple monitoring sensors.

[0011] Optionally, determining the cascaded screening order of the multiple monitoring sensors based on the operating condition information specifically includes:

[0012] The difference between the various operating condition parameters and their corresponding parameter thresholds is calculated, and the multiple difference results are normalized to obtain the overload coefficient of the various operating condition parameters.

[0013] Based on the overload coefficients of multiple operating parameters, the cascade screening order of multiple monitoring sensors is determined.

[0014] Optionally, the step of sequentially performing abnormal noise analysis and interruption condition judgment on the monitoring data of the multiple monitoring sensors according to the cascaded screening order of the multiple monitoring sensors specifically involves:

[0015] Based on the monitoring data of the second monitoring sensor, the abnormal noise of the reducer is located to obtain the first abnormal noise range. The second monitoring sensor is the first monitoring sensor in the cascade screening order.

[0016] The monitoring data within the first abnormal noise range are evaluated for quality to obtain a first quality score;

[0017] Determine whether the first quality score meets the interruption condition corresponding to the first quality score, wherein the interruption condition is that the first quality score is greater than or equal to its corresponding preset quality score.

[0018] Optionally, the abnormal noise location of the reducer based on the monitoring data from the second monitoring sensor to obtain the first abnormal noise range is specifically as follows:

[0019] The monitoring data from the first monitoring sensor is used to construct a gradient space;

[0020] Scan the gradient values ​​of multiple gradient points in the gradient space, and perform cluster analysis on the gradient values ​​of the multiple gradient points to obtain multiple clusters;

[0021] Calculate the gradient mean of the multiple clusters;

[0022] When the gradient mean of the first cluster is greater than or equal to the preset gradient mean, the spatial range of the first cluster in the gradient space is determined to be the first abnormal range, and the first cluster is any one of the multiple clusters.

[0023] Optionally, the step of sequentially performing abnormal noise analysis and interruption condition judgment on the monitoring data of the multiple monitoring sensors according to the cascaded screening order of the multiple monitoring sensors further includes:

[0024] If the first quality score does not meet the interruption condition corresponding to the first quality score, then based on the monitoring data of the third monitoring sensor, the abnormal noise is located in the first abnormal noise range to obtain the second abnormal noise range. The third monitoring sensor is the monitoring sensor that is one position after the second monitoring sensor in the cascade screening sequence.

[0025] The monitoring data within the second abnormal noise range are evaluated for quality to obtain a second quality score;

[0026] Determine whether the second quality score meets the interruption condition corresponding to the second quality score.

[0027] Optionally, the monitoring data within the first abnormal noise range is evaluated to obtain a first quality score, specifically as follows:

[0028] Obtain the signal-to-noise ratio and characteristic energy of the monitoring data within the first abnormal noise range;

[0029] Based on the various operating parameters, the signal-to-noise ratio weight and the feature energy weight are calculated.

[0030] The first quality score is obtained by calculating the weighted sum of the signal-to-noise ratio and the feature energy based on the signal-to-noise ratio weight and the feature energy weight.

[0031] Optionally, the calculation of the signal-to-noise ratio weight and feature energy weight based on various operating parameters is specifically as follows:

[0032]

[0033]

[0034] in, For signal-to-noise ratio weights, For characteristic energy weights, For the i-th operating condition parameter, Let be the influence coefficient of the i-th operating condition parameter on the signal-to-noise ratio. Let be the influence coefficient of the i-th operating condition parameter on the characteristic energy, and n be the number of operating condition parameters.

[0035] Secondly, this application provides a noise monitoring system for a speed reducer. The system includes an acquisition module, a processing module, and an output module, wherein:

[0036] The acquisition module is used to acquire the operating condition information of the reducer and the monitoring data of multiple monitoring sensors. The operating condition information includes various operating condition parameters.

[0037] The processing module is used to determine the cascaded screening order of multiple monitoring sensors based on the operating condition information; and to perform abnormal noise analysis and interruption condition judgment on the monitoring data of multiple monitoring sensors in sequence according to the cascaded screening order of multiple monitoring sensors.

[0038] The output module is used to output the monitoring data of the first monitoring sensor as target monitoring data if the abnormal noise analysis result of the first monitoring sensor meets its corresponding interruption condition, thereby improving the location analysis efficiency of subsequent fault analysis. The first monitoring sensor is any one of the multiple monitoring sensors.

[0039] Thirdly, this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0040] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the first aspects.

[0041] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0042] This application analyzes the operating condition information of the speed reducer to determine its operating status. Since the data quality detected by different types of monitoring sensors varies under different operating conditions—for example, if the ambient noise is high, the abnormal noise of the speed reducer will be masked, resulting in lower data quality from the sound sensor—the abnormal noise will increase the load on the gears, thus increasing the vibration frequency of the equipment. Since ambient noise has a relatively small impact on vibration, the data quality detected by the vibration sensor will be higher. Based on this principle, a cascaded screening order can be set for multiple different types of monitoring sensors according to the current operating condition. Multiple monitoring sensors then detect abnormal noise according to this cascaded screening order, continuously narrowing down the range of abnormal noise and allowing each monitoring sensor to focus on detecting local abnormal noise information. At this point, for some monitoring sensors, the overall data quality may be poor, but the local data quality may be better. Therefore, during the cascaded screening process, if a monitoring sensor detects high-quality data locally, the local monitoring data of that sensor can be directly output, thereby improving the efficiency of subsequent fault location analysis. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating a method for monitoring abnormal noise in a speed reducer, as provided in an embodiment of this application.

[0044] Figure 2 This is a schematic diagram of the structure of a speed reducer abnormal noise monitoring system provided in an embodiment of this application.

[0045] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0046] Explanation of reference numerals in the attached drawings: 1. Acquisition module; 2. Processing module; 3. Output module; 300. Electronic device; 301. Processor; 302. Communication bus; 303. User interface; 304. Network interface; 305. Memory. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0048] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0049] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0050] In actual industrial production, the stable operation of a production line depends on the close coordination of all its components. As a key device for power transmission and speed regulation, a malfunction in a speed reducer can potentially bring the entire production line to a standstill, resulting in significant economic losses. Abnormal noises are a typical precursor to early speed reducer failures. Many speed reducer malfunctions manifest in unusual sounds in their initial stages; for example, gear wear and bearing damage can produce sounds different from normal operation. Therefore, monitoring abnormal noises in speed reducers is of great significance for enabling predictive maintenance and reducing operating costs.

[0051] Currently, most gearbox noise monitoring still relies on a single sensor. Single-sensor monitoring has advantages such as low cost and simple installation, and can meet the basic monitoring needs of some enterprises to a certain extent. However, in actual industrial environments, abnormal noise signals are often mixed with the normal operating noise of equipment. There are various kinds of mechanical equipment in industrial sites, which generate noise of different frequencies and intensities during operation. These noises can interfere with the accurate capture of abnormal noise signals. Moreover, the normal operating noise of equipment will also vary under different operating conditions, which further increases the difficulty of abnormal noise signal identification, resulting in a high false alarm rate.

[0052] To address the aforementioned problems, this application provides a method for monitoring abnormal noise in a speed reducer. This method is applied to an abnormal noise monitoring system, such as... Figure 1 As shown, the method includes steps S101 to S104, which are as follows:

[0053] S101. Obtain the operating condition information of the reducer and the monitoring data of multiple monitoring sensors. The operating condition information includes various operating condition parameters.

[0054] In the above steps, various operating parameters include, but are not limited to, the reducer's load, speed, and operating time. Multiple monitoring sensors monitor different data, including vibration sensors, sound sensors, and temperature sensors. When the reducer starts operating, the abnormal noise monitoring system obtains the reducer's operating condition information by reading the control parameters in the reducer control system, and simultaneously receives continuously uploaded detection data from multiple sensors.

[0055] S102. Determine the cascade screening order of multiple monitoring sensors based on the operating condition information.

[0056] In the above steps, the current operating condition of the reducer can be determined from the operating condition information. Under different operating conditions, the quality of the data detected by different types of monitoring sensors will be different. For example, when the load of the reducer is large or the speed is high, the impact and vibration between the transmission components are strong. At this time, the quality of the data detected by the vibration sensor and the sound sensor will be high. When the reducer has been working for a long time, the transmission components will enter a fatigue state, and the impact and vibration between the transmission components will be reduced. However, due to the continuous operation, the temperature of its parts will continue to rise. At this time, the quality of the data detected by the temperature sensor will be high. Therefore, this application adjusts the monitoring order of multiple different types of monitoring sensors based on the current operating condition information of the reducer. If there is abnormal noise in the reducer, the monitoring sensor with higher detection data quality will be prioritized to continuously narrow down the possible range of abnormal noise, while the monitoring sensor with lower detection data quality will focus on detecting local abnormal noise information. It should be noted that the monitoring sensor with lower detection data quality does not mean that the sensor itself is defective, but that the detected data contains too much noise, resulting in low data quality. In the subsequent fault analysis process, this low-quality monitoring data will consume more computing resources for noise reduction processing, thereby greatly reducing the efficiency of localization analysis. However, although the data from the sensor with lower detection data quality may not perform well when detecting abnormal noise in the reducer as a whole, when focusing on a local area, the data quality of the detected data will be improved because some noise interference has been eliminated in advance.

[0057] When determining the cascade screening order of multiple monitoring sensors, this application calculates the difference between various operating parameters and their corresponding parameter thresholds, and normalizes the calculated difference results to obtain the overload coefficient of various operating parameters. It should be noted that when the operating parameters are closer to the parameter thresholds, or even exceed the parameter thresholds, the probability of abnormal noise from the reducer is greater. At this time, the manifestation of abnormal noise can be determined based on the overload coefficient of multiple operating parameters, thereby determining the cascade screening order of multiple monitoring sensors. For example, when the overload coefficient of the load is the largest, the abnormal noise is most likely caused by strong impact between transmission components, and strong impact will produce very strong equipment vibration. In this case, the cascade screening order of vibration sensors is the first priority.

[0058] S103. Following the cascaded screening order of multiple monitoring sensors, perform abnormal noise analysis and interruption condition judgment on the monitoring data of multiple monitoring sensors in sequence.

[0059] S104. If the abnormal noise analysis result of the first monitoring sensor meets its corresponding interruption condition, the monitoring data of the first monitoring sensor is output as the target monitoring data to improve the location analysis efficiency of subsequent fault analysis. The first monitoring sensor can be any one of multiple monitoring sensors.

[0060] In steps S103 to S104 above, after determining the cascaded screening order of multiple monitoring sensors, the monitoring data of multiple sensors are sequentially analyzed for abnormal noise according to the order of high to low quality of the detection data. This allows for precise location of the abnormal noise source, directly providing monitoring data near the abnormal noise source for subsequent fault analysis, thereby improving the efficiency of abnormal noise location analysis in fault analysis. Specifically, the sequential analysis of abnormal noise from multiple monitoring sensors can be illustrated as follows:

[0061] Taking the first, second, and third monitoring sensors as an example, under the current operating conditions, the cascaded screening order is the second monitoring sensor, the third monitoring sensor, and the first monitoring sensor. Then, the monitoring data from the second monitoring sensor is used to locate the abnormal noise and determine the range of the first abnormal noise. The location method involves constructing a gradient space from the monitoring data of the second monitoring sensor, scanning the gradient values ​​in the gradient space, and performing cluster analysis to obtain multiple clusters. The average gradient of these clusters is then calculated. When the average gradient of a cluster is greater than or equal to a preset average gradient threshold, it indicates that the data points within that cluster are changing drastically. At this point, the... The spatial range of the cluster represents the potential location of the abnormal noise source. Then, the monitoring data within the first abnormal noise range is evaluated for quality to obtain a first quality score. It is then determined whether the first quality score meets the interruption condition. The interruption condition is that the first quality score is greater than or equal to its corresponding preset quality score. The preset quality score is determined by the data quality required for subsequent fault analysis. It can be understood that if the data quality score within the first abnormal noise range is greater than or equal to its corresponding preset quality score, subsequent fault analysis can directly determine the fault type and location of the abnormal noise source based on the monitoring data within the first abnormal noise range, without needing to analyze more monitoring data. Specifically, the evaluation of the monitoring data within the first abnormal noise range to obtain the first quality score is as follows:

[0062] The signal-to-noise ratio (SNR) and characteristic energy of the monitoring data within the first abnormal noise range are obtained. Both SNR and characteristic energy can be used to characterize the signal quality of the monitoring data. Characteristic energy is the energy distribution characteristic of the signal in a specific frequency band or time-frequency domain, and is usually used to describe key information of the signal. When the SNR cannot accurately reflect the signal quality, since the energy distribution characteristics of the signals corresponding to different fault modes in the time-frequency domain are unique, the anomaly analysis of specific structures can be performed by analyzing the characteristic energy. For example, the SNR of the monitoring data generated by gear wear and bearing spalling are very similar, but they are completely different in their characteristic energy distribution. Gear wear may cause a concentrated increase in energy in a specific mid-to-high frequency band, while bearing spalling may cause energy anomalies in another specific frequency range. By analyzing the differences in these characteristic energy distributions, it is possible to more accurately determine whether the equipment is in an abnormal state, and thus determine the quality of the monitoring signal.

[0063] Then, based on various operating condition parameters, the signal-to-noise ratio (SNR) weight and characteristic energy weight are calculated. It's important to note that different operating conditions have varying impacts on SNR and characteristic energy. Some conditions significantly affect SNR but have a smaller impact on characteristic energy, while others have the opposite effect, and some conditions even significantly affect both. For example, at higher speeds, the gear meshing frequency is higher, causing a shift in the gear's characteristic frequency, resulting in a significant change in characteristic energy. While higher speeds also increase environmental noise, they also increase signal energy, thus making the overall SNR tend to stabilize. Therefore, this application proposes the following calculation formula to describe the influence weights of various operating condition parameters on SNR and characteristic energy, dividing the signal quality of the monitored data into two parts: SNR and characteristic energy, thereby improving the accuracy of the quality scoring results.

[0064]

[0065]

[0066] in, For signal-to-noise ratio weights, For characteristic energy weights, For the i-th operating condition parameter, Let be the influence coefficient of the i-th operating condition parameter on the signal-to-noise ratio. Let be the influence coefficient of the i-th operating condition parameter on the characteristic energy, and n be the number of operating condition parameters.

[0067] In the above formula, These are the normalized operating parameters, used to unify the dimensions of various operating parameters. This can be understood as the degree of influence of the first operating condition parameter on signal quality. This can be understood as the degree of influence of the first operating parameter on the signal-to-noise ratio. This can be understood as the degree of influence of the first operating parameter on the characteristic energy. By summing the degree of influence of multiple operating parameters on the signal-to-noise ratio, the comprehensive signal-to-noise ratio weight under the influence of multiple operating parameters can be obtained. Then, by summing the degree of influence of multiple operating parameters on the characteristic energy, the comprehensive characteristic energy weight under the influence of multiple operating parameters can be obtained.

[0068] Then, the signal-to-noise ratio (SNR) weight and the feature energy weight are normalized. The normalized SNR weight is then multiplied by the SNR of the monitored data within the first abnormal noise range to obtain the SNR part's description score of signal quality. The normalized feature energy weight is then multiplied by the feature energy of the monitored data within the first abnormal noise range to obtain the feature energy part's description score of signal quality. Finally, the two description scores are added together to obtain the first quality score of the monitored data within the first abnormal noise range.

[0069] If the first quality score fails to meet the interruption condition, it indicates that the data quality of the monitoring data within the first abnormal noise range is low, and the abnormal noise range needs to be further narrowed to determine a more precise source of the abnormal noise. At this point, the abnormal noise is located using the monitoring data from the third monitoring sensor to obtain the second abnormal noise range. The method for locating the abnormal noise using the monitoring data from the second monitoring sensor is the same as that used for locating the abnormal noise using the monitoring data from the third monitoring sensor, and will not be described in detail here. It should be noted that when locating the abnormal noise using the monitoring data from the third monitoring sensor, the spatial area corresponding to the first abnormal noise range is first delineated from the monitoring data, and then the abnormal noise is located only within the spatial area corresponding to the first abnormal noise range. The first monitoring sensor is used to eliminate noise. Then, the monitoring data within the second abnormal noise range is evaluated to obtain a second quality score. The evaluation method is the same as that for the monitoring data within the first abnormal noise range, and will not be elaborated further here. If the second quality score still cannot meet the corresponding interruption adjustment, the monitoring data of the first monitoring sensor is used to locate the abnormal noise and obtain a third abnormal noise range. If the data quality of the monitoring data within the third abnormal noise range meets the corresponding interruption condition, the monitoring data within the third abnormal noise range in the monitoring data of the first monitoring sensor is output as the target monitoring data, which improves the reliability of subsequent fault analysis.

[0070] In one possible implementation, if the data quality of the first monitoring sensor still cannot meet its interruption condition, then according to the cascaded screening order of multiple monitoring sensors, starting from the third abnormal noise range, abnormal noise analysis and interruption condition judgment are performed on the monitoring data of multiple monitoring sensors in sequence until there is monitoring data that meets the interruption condition among the monitoring data of multiple monitoring sensors, then the calculation stops and the monitoring data that meets the interruption condition is output.

[0071] Reference Figure 2 This application also provides a noise monitoring system for a speed reducer. The system includes an acquisition module 1, a processing module 2, and an output module 3, wherein:

[0072] The acquisition module 1 is used to acquire the operating condition information of the reducer and the monitoring data of multiple monitoring sensors. The operating condition information includes various operating condition parameters.

[0073] Processing module 2 is used to determine the cascade screening order of multiple monitoring sensors based on the working condition information; and to perform abnormal noise analysis and interruption condition judgment on the monitoring data of multiple monitoring sensors in sequence according to the cascade screening order of multiple monitoring sensors.

[0074] Output module 3 is used to output the monitoring data of the first monitoring sensor as the target monitoring data if the abnormal noise analysis result of the first monitoring sensor meets its corresponding interruption condition, thereby improving the location analysis efficiency of subsequent fault analysis. The first monitoring sensor can be any one of multiple monitoring sensors.

[0075] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0076] This application also discloses an electronic device. (See reference...) Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0077] The communication bus 302 is used to enable communication between these components.

[0078] The user interface 303 may include a display screen and a camera. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0079] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0080] The processor 301 may include one or more processing cores. The processor 301 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 305, and by calling data stored in memory 305. Optionally, the processor 301 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 301 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 301 and may be implemented as a separate chip.

[0081] The memory 305 may include random access memory (RAM) or read-only memory. Optionally, the memory 305 may include a non-transitory computer-readable storage medium. The memory 305 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 305 may also be at least one storage device located remotely from the aforementioned processor 301. (Refer to...) Figure 3 The memory 305, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for a method of monitoring abnormal noise in a speed reducer.

[0082] exist Figure 3In the illustrated electronic device 300, the user interface 303 is mainly used to provide an input interface for the user and acquire user input data; while the processor 301 can be used to call an application program stored in the memory 305 for monitoring abnormal noise of a speed reducer. When executed by one or more processors 301, the electronic device 300 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0084] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0085] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0086] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0087] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0088] The above description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0089] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for monitoring abnormal noise in a speed reducer, characterized in that, The method, applied to an abnormal noise monitoring system, includes: The operating condition information of the speed reducer and the monitoring data of multiple monitoring sensors are obtained. The operating condition information includes various operating parameters, and the multiple monitoring sensors are sensors that monitor different data. Based on the operating condition information, the cascading selection order of the multiple monitoring sensors is determined, specifically including: The difference between the various operating condition parameters and their corresponding parameter thresholds is calculated, and the multiple difference results are normalized to obtain the overload coefficient of the various operating condition parameters. Based on the overload coefficients of multiple operating parameters, the cascade screening order of multiple monitoring sensors is determined; According to the cascaded screening order of the multiple monitoring sensors, the monitoring data of the multiple monitoring sensors are sequentially analyzed for abnormal noise and interruption conditions are determined. If the abnormal noise analysis result of the first monitoring sensor meets its corresponding interruption condition, the monitoring data of the first monitoring sensor is output as the target monitoring data, thereby improving the location analysis efficiency of subsequent fault analysis. The first monitoring sensor is any one of the multiple monitoring sensors.

2. The method according to claim 1, characterized in that, The step of sequentially analyzing abnormal noises and determining interruption conditions based on the cascaded screening order of the multiple monitoring sensors is as follows: Based on the monitoring data of the second monitoring sensor, the abnormal noise of the reducer is located to obtain the first abnormal noise range. The second monitoring sensor is the first monitoring sensor in the cascade screening order. The monitoring data within the first abnormal noise range are evaluated for quality to obtain a first quality score; Determine whether the first quality score meets the interruption condition corresponding to the first quality score, wherein the interruption condition is that the first quality score is greater than or equal to its corresponding preset quality score.

3. The method according to claim 2, characterized in that, Based on the monitoring data from the second monitoring sensor, the abnormal noise of the reducer is located to obtain the first abnormal noise range, specifically as follows: The monitoring data from the first monitoring sensor is used to construct a gradient space; Scan the gradient values ​​of multiple gradient points in the gradient space, and perform cluster analysis on the gradient values ​​of the multiple gradient points to obtain multiple clusters; Calculate the gradient mean of the multiple clusters; When the gradient mean of the first cluster is greater than or equal to the preset gradient mean, the spatial range of the first cluster in the gradient space is determined to be the first abnormal range, and the first cluster is any one of the multiple clusters.

4. The method according to claim 2, characterized in that, The step of sequentially analyzing abnormal noises and determining interruption conditions based on the cascaded screening order of the multiple monitoring sensors also includes: If the first quality score does not meet the interruption condition corresponding to the first quality score, then based on the monitoring data of the third monitoring sensor, the abnormal noise is located in the first abnormal noise range to obtain the second abnormal noise range. The third monitoring sensor is the monitoring sensor that is one position after the second monitoring sensor in the cascade screening sequence. The monitoring data within the second abnormal noise range are evaluated for quality to obtain a second quality score; Determine whether the second quality score meets the interruption condition corresponding to the second quality score.

5. The method according to claim 2, characterized in that, The monitoring data within the first abnormal noise range are evaluated for quality to obtain a first quality score, specifically: Obtain the signal-to-noise ratio and characteristic energy of the monitoring data within the first abnormal noise range; Based on the various operating parameters, the signal-to-noise ratio weight and the feature energy weight are calculated. Based on the signal-to-noise ratio weight and the feature energy weight, the weighted sum of the signal-to-noise ratio and the feature energy is calculated to obtain the first quality score.

6. The method according to claim 5, characterized in that, The signal-to-noise ratio weight and feature energy weight are calculated based on various operating parameters, specifically as follows: ； ； in, For signal-to-noise ratio weights, For characteristic energy weights, For the i-th operating condition parameter, Let be the influence coefficient of the i-th operating condition parameter on the signal-to-noise ratio. Let be the influence coefficient of the i-th operating condition parameter on the characteristic energy, and n be the number of operating condition parameters.

7. A noise monitoring system for a speed reducer, characterized in that, The system is an abnormal noise monitoring system, which includes an acquisition module (1), a processing module (2), and an output module (3), wherein: The acquisition module (1) is used to acquire the operating condition information of the reducer and the monitoring data of multiple monitoring sensors. The operating condition information includes multiple operating condition parameters, and the multiple monitoring sensors are sensors that monitor different data. The processing module (2) is used to determine the cascade screening order of multiple monitoring sensors based on the operating condition information. Specifically, it includes: calculating the difference between multiple operating condition parameters and their corresponding parameter thresholds, and normalizing the multiple difference results to obtain the overload coefficient of multiple operating condition parameters; determining the cascade screening order of multiple monitoring sensors based on the overload coefficient of multiple operating condition parameters; and performing abnormal noise analysis and interruption condition judgment on the monitoring data of multiple monitoring sensors in sequence according to the cascade screening order of multiple monitoring sensors. The output module (3) is used to output the monitoring data of the first monitoring sensor as target monitoring data if the abnormal noise analysis result of the first monitoring sensor meets its corresponding interruption condition, thereby improving the location analysis efficiency of subsequent fault analysis. The first monitoring sensor is any one of the multiple monitoring sensors.

8. An electronic device, characterized in that, The device includes a processor (301), a memory (305), a user interface (303), and a network interface (304). The memory (305) is used to store instructions. The user interface (303) and the network interface (304) are used to communicate with other devices. The processor (301) is used to execute the instructions stored in the memory (305) to cause the electronic device to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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