CN-122020595-A - Method and system for analyzing association of rotation flexibility test data and assembly parameters of self-aligning roller bearing

CN122020595ACN 122020595 ACN122020595 ACN 122020595ACN-122020595-A

Abstract

The invention discloses a method and a system for analyzing association of rotation flexibility test data and assembly parameters of a self-aligning roller bearing. The method collects multidimensional time sequence data of bearing assembly parameters and rotation test through a system, and utilizes a machine learning algorithm to construct a quantitative association model between the bearing assembly parameters and the rotation test. Based on the model, the abnormal test data can be reversely analyzed, the assembly deviation source with poor flexibility is accurately positioned, the target parameters can be input in the process design stage, and the expected performance of the bearing can be forward predicted. The corresponding system integrates data acquisition, model analysis, application output and process feedback control modules and is integrated with a production line control system. The invention converts the traditional qualitative judgment depending on experience into the quantitative analysis driven by data and a model, realizes the intelligent closed loop from quality detection, root diagnosis to process optimization, and remarkably improves the consistency of the assembly quality of the bearing and the intelligent level of process control.

Inventors

REN JIANLONG
HAN LULU
REN XINGJIE

Assignees

临清市方特轴承有限公司

Dates

Publication Date: 20260512
Application Date: 20260205

Claims (10)

1. The method for analyzing the association of the rotation flexibility test data and the assembly parameters of the self-aligning roller bearing is characterized by comprising the following steps of: s1, acquiring an assembly parameter set of a batch of self-aligning roller bearings to be analyzed and a test data sequence acquired by each bearing in a rotation flexibility test, wherein the assembly parameter set at least comprises any two of play, retainer pressing amount and grease injection amount; S2, constructing an association model, namely taking the assembly parameter set as an input characteristic, taking a quantization index which is extracted from the test data sequence and represents rotation flexibility as an output target, training through a machine learning algorithm, and constructing the association model capable of quantitatively reflecting the influence of the assembly parameter on the rotation flexibility; and S3, analyzing and applying the association model to new bearing test data or target assembly parameters to generate an analysis result for positioning an assembly deviation source or evaluating the performance of an assembly scheme so as to guide the adjustment of an assembly process.
2. The method according to claim 1, wherein in step S1, the test data sequence includes torque values, vibration signals and/or sound signals that vary with time or angle.
3. The method for analyzing the association of the rotational flexibility test data and the assembly parameters of the self-aligning roller bearing according to claim 2, wherein the step S1 further comprises extracting features of the test data sequence to obtain a feature data set, wherein the features at least comprise a starting torque average value, a rotational stability index and a feature frequency amplitude.
4. The method according to claim 1, wherein in step S2, the machine learning algorithm is a supervised learning algorithm.
5. The method for analyzing the rotational flexibility test data and the assembly parameters of the self-aligning roller bearing according to claim 1, wherein the step S3 includes at least one of the following application modes: S31, inputting new bearing test data into the correlation model, reversely estimating corresponding predicted assembly parameters, and comparing the predicted assembly parameters with process design standard values to locate an assembly deviation source with abnormal rotation flexibility; And/or the number of the groups of groups, S32, a forward prediction mode, namely inputting target assembly parameters into the association model, and forward predicting corresponding prediction rotation flexibility indexes so as to evaluate whether the expected performance of the bearing under the assembly scheme meets the standard.
6. The method for analyzing the rotational flexibility test data and the assembly parameters of the self-aligning roller bearing according to claim 1, wherein the step S3 further comprises: And S4, a process iteration optimization step, namely dynamically adjusting control parameters of corresponding stations in the assembly line according to the analysis result.
7. A self-aligning roller bearing rotational flexibility test data and assembly parameter correlation analysis system for implementing the self-aligning roller bearing rotational flexibility test data and assembly parameter correlation analysis method of any one of claims 1-6, comprising: The data acquisition and management module is used for acquiring and storing the assembly parameter set and the test data sequence in an associated manner from the bearing assembly line and the rotation flexibility test table; The model construction and analysis module is used for executing construction, storage and calling of the association model based on the data in the data acquisition and management module; and the application and output module is used for receiving new bearing test data or target assembly parameters, calling the correlation model for calculation and outputting an analysis result for guiding process adjustment.
8. The self-aligning roller bearing rotational flexibility test data and assembly parameter correlation analysis system of claim 7, further comprising: And the process feedback control module is connected with the application and output module and used for converting the analysis result into a specific process parameter adjustment instruction and sending the specific process parameter adjustment instruction to an assembly line control system.
9. The system for analyzing the association of the rotational flexibility test data and the assembly parameters of the self-aligning roller bearing according to claim 7, further comprising a man-machine interaction interface module for configuring model parameters, visually displaying the association of the assembly parameters and the test data, analyzing results and process adjustment suggestions.
10. The system for analyzing the rotational flexibility test data and the assembly parameters of the self-aligning roller bearing according to claim 7, wherein the system is deployed in the form of an integrated hardware platform or industrial software, the data acquisition and management module is in communication connection with a programmable logic controller of a bearing assembly line and a sensor and data acquisition card of a rotational flexibility test board through an industrial communication interface, and the application and output module performs data interaction with a manufacturing execution system or a database server through a workshop network.

Description

Method and system for analyzing association of rotation flexibility test data and assembly parameters of self-aligning roller bearing Technical Field The invention relates to the field of industrial intellectualization and predictive quality control, in particular to a method and a system for analyzing association of rotation flexibility test data and assembly parameters of a self-aligning roller bearing. Background The self-aligning roller bearing is used as a key basic part, and the rotation flexibility of the self-aligning roller bearing directly influences the operation precision, the energy consumption and the reliability of host equipment, so that the rotation flexibility test after assembly is an important quality inspection link before bearing delivery. At present, a mode of manual rotation sensory evaluation or simple instrument measurement friction torque is commonly adopted in the industry, the test result is judged whether to be qualified or not or a single value, the process is highly dependent on the experience of operators, the test data are independently dispersed, and effective correlation cannot be formed between the test data and assembly parameters (such as play, retainer pressing amount, grease injection amount and the like) of the bearing. The traditional method is difficult to realize quantitative analysis and data tracing, and cannot provide data support for precise optimization of the assembly process. With the development of intelligent manufacturing and digital detection technology, the bearing industry is urgent to establish a deep connection between test data and an assembly process. In the prior art, a systematic method is lacking to perform associated modeling and analysis on multidimensional dynamic data (such as starting torque, rotation uniformity, abnormal sound frequency spectrum and the like) acquired in the rotation flexibility test process and key parameters of bearing assembly. The root of the assembly quality problem is difficult to trace, and the lack of data basis for process adjustment restricts the further improvement of the consistency and the reliability of the bearing. Therefore, development of the method and the system capable of deeply fusing test data and assembly parameters and realizing quantitative association and intelligent analysis has important significance in realizing accurate assembly and improving product quality. Disclosure of Invention The invention particularly relates to a method and a system for analyzing association of rotation flexibility test data and assembly parameters of a self-aligning roller bearing, which aim to collect the assembly parameters and multidimensional test data of the bearing through the system and construct a quantitative association model by utilizing a machine learning algorithm, so that the span from subjective experience judgment to objective data driving analysis of the rotation flexibility is realized, and the accuracy and consistency of quality assessment are remarkably improved. In order to achieve the above purpose, the specific technical scheme of the method and the system for analyzing the association of the rotation flexibility test data and the assembly parameters of the self-aligning roller bearing is as follows: a method for analyzing the association of rotation flexibility test data and assembly parameters of a self-aligning roller bearing comprises the following steps: s1, acquiring an assembly parameter set of a batch of self-aligning roller bearings to be analyzed and a test data sequence acquired by each bearing in a rotation flexibility test, wherein the assembly parameter set at least comprises any two of play, retainer pressing amount and grease injection amount; S2, constructing an association model, namely taking the assembly parameter set as an input characteristic, taking a quantization index which is extracted from the test data sequence and represents rotation flexibility as an output target, training through a machine learning algorithm, and constructing the association model capable of quantitatively reflecting the influence of the assembly parameter on the rotation flexibility; and S3, analyzing and applying the association model to new bearing test data or target assembly parameters to generate an analysis result for positioning an assembly deviation source or evaluating the performance of an assembly scheme so as to guide the adjustment of an assembly process. Further, in the step S1, the test data sequence includes a torque value, a vibration signal and/or a sound signal that varies with time or angle. Further, the step S1 further comprises feature extraction of the test data sequence to obtain a feature data set, wherein the features at least comprise a starting torque mean value, a rotation stability index and a feature frequency amplitude. Further, in the step S2, the machine learning algorithm is a supervised learning algorithm. Further, the step S3 includes at least one of the fo