Rolling bearing is of great importance in rotating machinery, so the fault diagnosis of rolling bearing is essential to ensure safe operations. The traditional diagnosis approach based on characteristic frequency was shown to be not consistent with experimental data in some cases. Furthermore, two data sets measured under the same circumstance gave different characteristic frequency results, and the harmonic frequency was not linearly proportional to the fundamental frequency. These indicate that existing fault diagnosis is inaccurate and not reliable. This work introduced a new method based on data-driven random fuzzy evidence acquisition and Dempster–Shafer evidence theory, which first compared fault sample data with fuzzy expert system, followed by the determination of random likelihood value and finally obtained diagnosis conclusion based on the data fusion rule. This method was proved to have high accuracy and reliability with a good agreement with experimental data, thus providing a new theoretical approach to fuzzy information processing in complicated numerically controlled equipments.
The industrial robot is a mechanized electronic device that functions as a human arm, wrist and hand. Rolling bearings are an essential part of these flexible rotation. Due to the harsh environment of the industrial robot and full-load operation, bearing faults are difficult to be diagnose and occur from time to time. In the long-term research, the author found that the traditional method based on fault characteristic frequency has at least two problems. On the one hand, some bearing parameters are not easy to obtain so that the fault characteristic frequency cannot be calculated, especially the high-precision bearing of some imported equipment. On the other hand, some bearings can calculate the fault characteristic frequency, but the fuzziness of the method is difficult to overcome. This paper introduces a new method based on experimental data-driven random fuzzy evidence acquisition and intuitionistic fuzzy sets fusion (IFSF). Firstly, this method does not need to calculate the fault characteristic frequency, by constructing and matching the fuzzy expert system and the sample to be tested. The maximum value of the vertical coordinate of the intersection point, namely the likelihood measure value, is used as the membership degree of the support proposition. Then, the essential meaning of uncertainty parameter is analyzed, the membership degree of the fuzzy set under the random set framework is transformed into the membership function of the intuitionistic fuzzy set, and the binary likelihood pair is used to represent the single likelihood measure value. Finally, the single sensor multi-feature fusion and multi-sensor information fusion are transformed into intuitionistic fuzzy set multiattribute decision fusion. The experimental results show that the method proposed in this paper can overcome the fuzziness of the traditional method, and provides a new theoretical method for the fault diagnosis of rolling bearing, which is difficult to obtain geometric parameters.INDEX TERMS Data-driven, random fuzzy set, intuitionistic fuzzy sets, data fusion rule.
The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R2), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ultimate bearing capacity of pile foundations.
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