Abnormal sound monitoring method and system for speed reducer

Abstract

The invention discloses an abnormal sound monitoring method and system for a speed reducer, and relates to the field of transmission device fault detection. The method is applied to an abnormal sound monitoring system and comprises the steps that working condition information of a speed reducer and monitoring data of a plurality of monitoring sensors are obtained, and the working condition information comprises multiple working condition parameters; determining a cascade screening sequence of the plurality of monitoring sensors according to the working condition information; performing abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors in sequence according to a cascade screening sequence of the plurality of monitoring sensors; and if the abnormal sound analysis result of the first monitoring sensor meets the corresponding interruption condition, the monitoring data of the first monitoring sensor are output as target monitoring data, so that the positioning analysis efficiency of subsequent fault analysis is improved, and the first monitoring sensor is any one of a plurality of monitoring sensors. By implementing the technical scheme provided by the invention, the problem of low abnormal sound monitoring precision of the traditional speed reducer is solved.

Description

Technical Field

[0001] The present application relates to the technical field of transmission device fault detection, and in particular to a method and system for monitoring abnormal noise of a reducer. Background Art

[0002] As a core component of the industrial transmission system, the health of the reducer is directly related to the continuity of the production line and the safety of the equipment. Abnormal noise is a typical precursor to early failure of the reducer. Therefore, abnormal noise monitoring of the reducer is of great significance for achieving predictive maintenance and reducing operation and maintenance costs.

[0003] At present, most reducer abnormal noise monitoring still relies on a single sensor, but the abnormal noise signal is often mixed with the normal operation 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 noise of reducers. Summary of the Invention

[0005] In order to solve the problem of low accuracy of traditional abnormal noise monitoring of reducers, the present application provides a method and system for abnormal noise monitoring of reducers.

[0006] In a first aspect, the present application provides a method for monitoring abnormal noise of a reducer, which is applied to an abnormal noise monitoring system, and the method comprises: Acquire the working condition information of the reducer and monitoring data of multiple monitoring sensors, wherein the working condition information includes multiple working condition parameters; Determining a cascade screening order of the plurality of monitoring sensors according to the operating condition information; According to the cascade screening order of the plurality of monitoring sensors, performing abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors in turn; If the abnormal sound 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, so as to improve the positioning analysis efficiency of subsequent fault analysis. The first monitoring sensor is any one of the multiple monitoring sensors.

[0007] Optionally, determining the cascade screening order of the plurality of monitoring sensors according to the operating condition information specifically includes: performing difference calculations on the plurality of operating parameters and their corresponding parameter thresholds, and normalizing the plurality of difference results to obtain overload coefficients of the plurality of operating parameters; Based on the overload coefficients of the multiple operating parameters, the cascade screening order of the multiple monitoring sensors is determined.

[0008] Optionally, the abnormal sound analysis and interruption condition judgment are performed on the monitoring data of the multiple monitoring sensors in sequence according to the cascade screening order of the multiple monitoring sensors, specifically: Based on the monitoring data of the second monitoring sensor, the reducer is located to obtain a first abnormal sound range, wherein the second monitoring sensor is the first monitoring sensor in the cascade screening order; Performing a quality evaluation on the monitoring data within the first abnormal sound range to obtain a first quality score; It is determined whether the first quality score meets a termination condition corresponding to the first quality score, where the termination condition is that the first quality score is greater than or equal to a corresponding preset quality score.

[0009] Optionally, based on the monitoring data of the second monitoring sensor, the reducer is subjected to abnormal noise positioning to obtain a first abnormal noise range, specifically: constructing the monitoring data of the first monitoring sensor into a gradient space; Scanning gradient values of a plurality of gradient points in the gradient space, and performing cluster analysis on the gradient values of the plurality of gradient points to obtain a plurality of clusters; Calculating the gradient mean of the plurality of clusters; When the gradient mean of the first cluster is greater than or equal to the preset gradient mean, it is determined that the spatial range of the first cluster in the gradient space is the first abnormal sound range, and the first cluster is any one of the multiple clusters.

[0010] Optionally, the step of performing abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors in sequence according to the cascade screening order of the plurality of monitoring sensors further 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 reducer is located within the first abnormal sound range to obtain a second abnormal sound range, and the third monitoring sensor is a monitoring sensor that is one position after the second monitoring sensor in the cascade screening order; Performing a quality evaluation on the monitoring data within the second abnormal sound range to obtain a second quality score; It is determined whether the second quality score satisfies an interruption condition corresponding to the second quality score.

[0011] Optionally, a quality evaluation is performed on the monitoring data within the first abnormal sound range to obtain a first quality score, specifically: Obtaining a signal-to-noise ratio and characteristic energy of monitoring data within the first abnormal sound range; Calculating a signal-to-noise ratio weight and a characteristic energy weight according to the various operating parameters; According to the signal-to-noise ratio weight and the feature energy weight, a weighted sum of the signal-to-noise ratio and the feature energy is calculated to obtain the first quality score.

[0012] Optionally, the signal-to-noise ratio weight and the characteristic energy weight are calculated based on the multiple operating condition parameters, specifically: in, is the signal-to-noise ratio weight, is the feature energy weight, is the i-th working condition parameter, is the influence coefficient of the i-th operating condition parameter on the signal-to-noise ratio, is the influence coefficient of the i-th operating parameter on the characteristic energy, and n is the number of operating parameters.

[0013] In a second aspect, the present application provides a speed reducer abnormal noise monitoring system, the system being an abnormal noise monitoring system, the system comprising an acquisition module, a processing module, and an output module, wherein: The acquisition module is used to acquire the working condition information of the reducer and the monitoring data of multiple monitoring sensors, wherein the working condition information includes multiple working condition parameters; The processing module is configured to determine a cascade screening order of the plurality of monitoring sensors according to the operating condition information; and perform abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors in sequence according to the cascade screening order of the plurality of monitoring sensors; The output module is used to output the monitoring data of the first monitoring sensor as target monitoring data if the abnormal sound analysis result of the first monitoring sensor meets its corresponding interruption condition, so as to improve the positioning analysis efficiency of subsequent fault analysis. The first monitoring data sensor is any one of the multiple monitoring sensors.

[0014] In a third aspect, the present application provides an electronic device comprising a processor, a memory, a user interface, and a network interface, wherein 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 so that the electronic device executes a method as described in any one of the first aspects.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method as described in any one of the first aspects is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: The present application determines the working condition of the reducer by analyzing the working condition information of the reducer. Due to different working conditions, the data quality detected by different types of monitoring sensors is different. For example, if the working environment noise is large, the abnormal sound of the reducer will be drowned out, resulting in the data quality detected by the sound sensor being low. However, the abnormal sound will increase the load on the gear, thereby increasing the vibration frequency of the equipment, while the impact of environmental noise on vibration is small, and the data quality detected by the vibration sensor at this time will be higher. Based on this principle, according to the current working conditions, a cascade screening order can be set for multiple different types of monitoring sensors. At this time, multiple monitoring sensors detect abnormal sounds according to the cascade screening order, continuously narrowing the range of abnormal sounds, so that each monitoring sensor focuses on detecting local abnormal sound information. At this time, for some monitoring sensors, the overall detection data quality may be poor, but the local data quality may be better. Therefore, in the cascade screening process, if the monitoring data quality of a monitoring sensor in the local detection is high, the local monitoring data of the monitoring sensor can be directly output, thereby improving the positioning analysis efficiency of subsequent fault analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a method for monitoring abnormal noise of a reducer provided in an embodiment of the present application.

[0018] Figure 2 It is a structural schematic diagram of an abnormal noise monitoring system for a reducer provided in an embodiment of the present application.

[0019] Figure 3 This is a structural diagram of an electronic device provided in an embodiment of the present application.

[0020] Explanation of the accompanying 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 DESCRIPTION

[0021] In order 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0022] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0023] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0024] In actual industrial production, the stable operation of a production line depends on the close coordination of all links. Reducers, as key devices for power transmission and speed regulation, are very likely to stall the entire production line if they fail, resulting in significant economic losses. Abnormal noise is a typical precursor to early-stage reducer failures. Many reducer failures manifest themselves in unusual noises in the early stages. For example, problems such as gear wear and bearing damage can produce noises that differ from normal operation. Therefore, monitoring reducer noise is crucial for predictive maintenance and reducing operational costs.

[0025] At present, most reducer abnormal noise monitoring still relies on a single sensor. The single sensor monitoring method has the advantages of low cost and easy installation, and can meet the basic monitoring needs of some companies for reducers to a certain extent. However, in the actual industrial environment, abnormal noise signals are often mixed with the normal operating noise of the equipment. There are various mechanical equipment on the industrial site. They will generate noise of different frequencies and intensities during operation. These noises will interfere with the accurate capture of abnormal noise signals. Moreover, the normal operating noise of the equipment under different working conditions will also vary, which further increases the difficulty of abnormal noise signal identification, resulting in a high false alarm rate.

[0026] In order to solve the above problems, the present application provides a method for monitoring abnormal noise of a reducer, which 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: S101. Obtain operating condition information of the reducer and monitoring data from multiple monitoring sensors, where the operating condition information includes multiple operating condition parameters.

[0027] In the above steps, the various operating parameters include, but are not limited to, the reducer's load, speed, and operating hours. The multiple monitoring sensors monitor different data, including vibration, sound, and temperature sensors. When the reducer begins operating, the abnormal sound monitoring system obtains operating information by reading the control parameters in the reducer control system and receiving continuous data uploaded by multiple monitoring systems, thereby obtaining monitoring data from multiple sensors.

[0028] S102: Determine a cascade screening order of multiple monitoring sensors according to working condition information.

[0029] In the above steps, the working condition of the current reducer can be determined by the working condition information, and the quality of the data detected by different types of monitoring sensors will be different under different working conditions of the reducer; for example, when the load of the reducer is large or the speed is fast, the impact and vibration between the transmission components are strong, and the quality of the data detected by the vibration sensor and the sound sensor will be higher; when the working time of the reducer is long, the transmission components will enter a fatigue state, and the impact and vibration between the transmission components will be weakened, but due to continuous operation, the temperature of the parts will continue to rise, and the quality of the data detected by the temperature sensor will be higher. Therefore, the present application adjusts the monitoring order of multiple different types of monitoring sensors according to the current operating condition information of the reducer. If there is an abnormal noise in the speed machine, the monitoring sensors with higher detection data quality will be given priority for detection, which will continuously narrow the possible range of the abnormal noise, so that the monitoring sensors with lower detection data quality can focus on detecting local abnormal noise information. It should be noted that the monitoring sensors with lower detection data quality do not mean that the sensors themselves have defects, 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 take up more computing resources for denoising, thereby greatly reducing the efficiency of positioning analysis; however, the sensors with lower detection data quality may perform poorly in the overall detection of abnormal noise in the reducer, but when focusing on the local area, the data quality of the detected data will also be improved because some noise interference has been eliminated in advance.

[0030] When determining the cascade screening order of multiple monitoring sensors, the present application obtains the overload coefficients of multiple operating parameters by performing difference calculations between multiple operating parameters and their corresponding parameter thresholds, and normalizing the multiple difference results obtained. It should be noted that the closer the operating parameters are to the parameter threshold, or even when they exceed the parameter threshold, the greater the probability of abnormal noise in the reducer. At this time, the manifestation of the abnormal noise can be determined based on the overload coefficients 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 to be caused by a strong impact between the transmission components, and the strong impact will produce very strong equipment vibration. At this time, the cascade screening order of the vibration sensor is first.

[0031] S103 , performing abnormal sound analysis and interruption condition judgment on the monitoring data of the multiple monitoring sensors in sequence according to the cascade screening order of the multiple monitoring sensors.

[0032] S104. If the abnormal sound 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, so as to improve the location analysis efficiency of subsequent fault analysis. The first monitoring sensor is any one of the multiple monitoring sensors.

[0033] In the above steps S103 to S104, after determining the cascade screening order of multiple monitoring sensors, the monitoring data of the multiple monitoring sensors are analyzed for abnormal sounds in descending order of detection data quality, so as to accurately locate the source of the abnormal sound, thereby directly providing monitoring data near the source of the abnormal sound for subsequent fault analysis, thereby improving the efficiency of abnormal sound location analysis in fault analysis. Specifically, the abnormal sound analysis of the monitoring data of the multiple monitoring sensors is performed in sequence, for example: Taking the first monitoring sensor, the second monitoring sensor and the third monitoring sensor as an example, under the current working conditions, the cascade screening order is the second monitoring sensor, the third monitoring sensor, and the first monitoring sensor; then the monitoring data of the second monitoring sensor is firstly located for abnormal sound, and the range of the first abnormal sound is determined. The positioning method is to construct the monitoring data of the second monitoring sensor into a gradient space, and then scan the gradient value in the gradient space, and perform cluster analysis to obtain multiple clusters, and then calculate the gradient mean of the multiple clusters. When the gradient mean of a cluster is greater than or equal to the preset gradient mean threshold, it means that the data points in the cluster change very drastically. At this time, the The spatial range of the cluster is the range space where the abnormal sound source may exist; then, the monitoring data within the first abnormal sound range is evaluated for quality to obtain a first quality score, and it is 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 by the subsequent fault analysis. It can be understood that if the data quality score within the first abnormal sound range is greater than or equal to its corresponding preset quality score, then the subsequent fault analysis can directly determine the fault type and fault location of the abnormal sound source based on the monitoring data within the first abnormal sound range, without the need to analyze more monitoring data. Among them, the monitoring data within the first abnormal sound range is evaluated for quality to obtain the first quality score, specifically: The signal-to-noise ratio and characteristic energy of the monitoring data within the first abnormal sound range are obtained. Both the signal-to-noise ratio and the characteristic energy can be used to characterize the signal quality of the monitoring data. The characteristic energy is the energy distribution characteristic of the signal in a specific frequency band or time-frequency domain, which is usually used to describe the key information of the signal. When the signal-to-noise ratio 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 characteristic energy can be analyzed to perform abnormal analysis on the specific structure. For example, the signal-to-noise ratios of the monitoring data generated by gear wear and bearing spalling are very similar, but they are completely different in characteristic energy distribution. Gear wear may lead to increased energy concentration in specific medium and high frequency bands, while bearing spalling may cause energy abnormalities in other specific frequency ranges. At this time, by analyzing the differences in these characteristic energy distributions, it is possible to more accurately judge whether the equipment is in an abnormal state, and then determine the quality of the monitoring signal.

[0034] Then, based on a variety of working condition parameters, the signal-to-noise ratio weight and characteristic energy weight are calculated. What needs to be explained here is that due to different working conditions, the impact on the signal-to-noise ratio and characteristic energy is different. Some working conditions will have a greater impact on the signal-to-noise ratio, but a smaller impact on the characteristic energy. Some working conditions will be the opposite, and even some working conditions will have a greater impact on both. For example, when the speed is high, the gear meshing frequency is high, which causes the characteristic frequency of the gear to shift, that is, the characteristic energy has changed significantly. Although the speed will also cause the environmental noise to increase, the signal energy will also increase, so that the overall signal-to-noise ratio tends to be stable. Therefore, this application proposes the following calculation formula to describe the influence weights of various working condition parameters on the signal-to-noise ratio and characteristic energy respectively, and divides the signal quality of the monitoring data into two parts: signal-to-noise ratio and characteristic energy for description, so as to improve the accuracy of the quality scoring results: in, is the signal-to-noise ratio weight, is the feature energy weight, is the i-th working condition parameter, is the influence coefficient of the i-th operating condition parameter on the signal-to-noise ratio, is the influence coefficient of the i-th operating parameter on the characteristic energy, and n is the number of operating parameters.

[0035] In the above formula, is the normalized working condition parameter, which unifies the dimensions of various working condition parameters. It can be understood as the influence of the first working condition parameter on the signal quality. It can be understood as the influence of the first working condition parameter on the signal-to-noise ratio. It can be understood as the degree of influence of the first operating parameter on the characteristic energy; by summing the influence of multiple operating parameters on the signal-to-noise ratio, the comprehensive signal-to-noise ratio weight of the signal-to-noise ratio under the influence of multiple operating parameters can be obtained, and then by summing the influence of multiple operating parameters on the characteristic energy, the comprehensive characteristic energy weight of the characteristic energy under the influence of multiple operating parameters can be obtained.

[0036] Then, the signal-to-noise ratio weight and the characteristic energy weight are normalized, and the normalized signal-to-noise ratio weight is multiplied by the signal-to-noise ratio of the monitoring data within the first abnormal sound range to obtain a description score of the signal quality from the signal-to-noise ratio part, and then the normalized characteristic energy weight is multiplied by the characteristic energy of the monitoring data within the first abnormal sound range to obtain a description score of the signal quality from the characteristic energy part. Finally, the description scores of the two are added together to obtain the first quality score of the monitoring data within the first abnormal sound range.

[0037] When the first quality score cannot meet the interruption condition, it means that the data quality of the monitoring data within the first abnormal sound range is low, and the abnormal sound range needs to be further narrowed to determine a more accurate abnormal sound source; at this time, the monitoring data of the third monitoring sensor is used to locate the abnormal sound, and the second abnormal sound range is obtained. The abnormal sound location method is consistent with the abnormal sound location method of the monitoring data of the second monitoring sensor, and no further description is given here; it should be noted here that when locating the abnormal sound of the monitoring data of the third monitoring sensor, the spatial area corresponding to the first abnormal sound range is first delineated from the monitoring data, and then the abnormal sound location is performed only in the spatial area corresponding to the first abnormal sound range. The second abnormal sound range is selected as the detection range of the first monitoring sensor, and the second abnormal sound range is selected as the detection range of the first monitoring sensor. The second abnormal sound range is selected as the detection range of the first monitoring sensor, and the third abnormal sound range is obtained. If the data quality of the monitoring data within the third abnormal sound range meets the corresponding interruption condition, the monitoring data within the third abnormal sound range of the monitoring data of the first monitoring sensor is output as the target monitoring data, which improves the reliability of subsequent fault analysis.

[0038] In one possible implementation, if the data quality of the first monitoring sensor still cannot meet its interruption condition, then according to the cascade screening order of multiple monitoring sensors, starting from the third abnormal sound range, the monitoring data of multiple monitoring sensors are analyzed for abnormal sounds and the interruption conditions are judged in turn until there is monitoring data that meets the interruption condition in the monitoring data of multiple monitoring sensors, the calculation is stopped, and the monitoring data that meets the interruption condition is output.

[0039] Reference Figure 2 The present application also provides a speed reducer abnormal noise monitoring system, which includes an acquisition module 1, a processing module 2, and an output module 3, wherein: Acquisition module 1, used to obtain the working condition information of the reducer and the monitoring data of multiple monitoring sensors, the working condition information includes multiple working condition parameters; Processing module 2 is used to determine the cascade screening order of multiple monitoring sensors according to the working condition information; according to the cascade screening order of the multiple monitoring sensors, perform abnormal sound analysis and interruption condition judgment on the monitoring data of the multiple monitoring sensors in sequence; Output module 3 is used to output the monitoring data of the first monitoring sensor as target monitoring data if the abnormal sound analysis result of the first monitoring sensor meets its corresponding interruption condition, so as to improve the positioning analysis efficiency of subsequent fault analysis. The first monitoring sensor is any one of multiple monitoring sensors.

[0040] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual 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 device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0041] This application also discloses an electronic device. Figure 3 , Figure 3 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 .

[0042] The communication bus 302 is used to implement the connection and communication between these components.

[0043] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0044] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0045] The processor 301 may include one or more processing cores. Using various interfaces and circuits, the processor 301 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 305, as well as accesses data stored in the memory 305, to perform various server functions and process data. Optionally, the processor 301 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may not be integrated into the processor 301 but implemented as a separate chip.

[0046] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also optionally be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a method for monitoring abnormal noise of a reducer.

[0047] exist Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program of a method for monitoring abnormal noise of a reducer stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0048] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0049] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0050] Units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0051] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0052] 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 memory. Based on this understanding, the technical solution of this application, or the portion 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 for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0053] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0054] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for monitoring abnormal noise of a reducer, characterized in that: Applied to an abnormal sound monitoring system, the method includes: Acquire the working condition information of the reducer and monitoring data of multiple monitoring sensors, wherein the working condition information includes multiple working condition parameters; Determining a cascade screening order of the plurality of monitoring sensors according to the operating condition information; According to the cascade screening order of the plurality of monitoring sensors, performing abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors in turn; If the abnormal sound 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, so as to improve the positioning 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 Determining the cascade screening order of the plurality of monitoring sensors according to the operating condition information specifically includes: performing difference calculations on the plurality of operating parameters and their corresponding parameter thresholds, and normalizing the plurality of difference results to obtain overload coefficients of the plurality of operating parameters; Based on the overload coefficients of the multiple operating parameters, the cascade screening order of the multiple monitoring sensors is determined.

3. The method according to claim 1, characterized in that According to the cascade screening order of the multiple monitoring sensors, the monitoring data of the multiple monitoring sensors are analyzed for abnormal sounds and the interruption conditions are judged in sequence, specifically: Based on the monitoring data of the second monitoring sensor, the reducer is located to obtain a first abnormal sound range, wherein the second monitoring sensor is the first monitoring sensor in the cascade screening order; Performing a quality evaluation on the monitoring data within the first abnormal sound range to obtain a first quality score; It is determined whether the first quality score meets a termination condition corresponding to the first quality score, where the termination condition is that the first quality score is greater than or equal to a corresponding preset quality score.

4. The method according to claim 3, characterized in that Based on the monitoring data of the second monitoring sensor, the reducer is subjected to abnormal noise positioning to obtain a first abnormal noise range, which is specifically: constructing the monitoring data of the first monitoring sensor into a gradient space; Scanning gradient values of a plurality of gradient points in the gradient space, and performing cluster analysis on the gradient values of the plurality of gradient points to obtain a plurality of clusters; Calculating the gradient mean of the plurality of clusters; When the gradient mean of the first cluster is greater than or equal to the preset gradient mean, it is determined that the spatial range of the first cluster in the gradient space is the first abnormal sound range, and the first cluster is any one of the multiple clusters.

5. The method according to claim 2, characterized in that The method of sequentially analyzing the abnormal sound and determining the interruption condition of the monitoring data of the plurality of monitoring sensors according to the cascade screening order of the plurality of monitoring sensors further 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 reducer is located within the first abnormal sound range to obtain a second abnormal sound range, and the third monitoring sensor is a monitoring sensor that is one position after the second monitoring sensor in the cascade screening order; Performing a quality evaluation on the monitoring data within the second abnormal sound range to obtain a second quality score; It is determined whether the second quality score satisfies an interruption condition corresponding to the second quality score.

6. The method according to claim 3, characterized in that Perform a quality evaluation on the monitoring data within the first abnormal sound range to obtain a first quality score, specifically: Obtaining a signal-to-noise ratio and characteristic energy of monitoring data within the first abnormal sound range; Calculating a signal-to-noise ratio weight and a characteristic energy weight according to the various operating parameters; According to the signal-to-noise ratio weight and the feature energy weight, a weighted sum of the signal-to-noise ratio and the feature energy is calculated to obtain the first quality score.

7. The method according to claim 6, characterized in that The signal-to-noise ratio weight and characteristic energy weight are calculated based on the various operating parameters, specifically: in, is the signal-to-noise ratio weight, is the feature energy weight, is the i-th working condition parameter, is the influence coefficient of the i-th operating condition parameter on the signal-to-noise ratio, is the influence coefficient of the i-th operating parameter on the characteristic energy, and n is the number of operating parameters.

8. A noise monitoring system for a reducer, characterized in that: The system is an abnormal sound monitoring system, comprising an acquisition module (1), a processing module (2) and an output module (3), wherein: The acquisition module (1) is used to acquire the working condition information of the reducer and the monitoring data of multiple monitoring sensors, wherein the working condition information includes multiple working condition parameters; The processing module (2) is used to determine the cascade screening order of the plurality of monitoring sensors according to the working condition information; and perform abnormal sound analysis and interruption condition judgment on the monitoring data of the plurality of monitoring sensors in sequence according to the cascade screening order of the plurality of 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 sound analysis result of the first monitoring sensor meets its corresponding interruption condition, thereby improving the location analysis efficiency of subsequent fault analysis, and the first monitoring sensor is any one of the multiple monitoring sensors.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is performed.

Images