基于Logistic回归模型的TC4零件激光熔化沉积工艺参数分析（英文）

In this paper, laser melting deposition(LMD), a new advanced manufacture technology. While manufacturing a metal part by LMD process, if we could control the energy distribution in internal different areas such as cladding layer or that between cladding layer and the substrate with optimal process parameters, the probability of internal defects of parts can be reduced, and the mechanical properties of parts will be greatly improved. To address the problem that whether the part made by LMD has internal defects, in this paper we designed the orthogonal rotation experiments through selecting different process parameters. Then a Logistic Regression model was built based on the experiments data. The calculation result of the regression model was in good agreement with the result of authentication test. Therefore, this Logistic Regression model has important reference for selecting LMD process parameters.

Anomaly detection of hot components in gas turbine based on frequent pattern extraction

Hot components operate in a high-temperature and high-pressure environment. The occurrence of a fault in hot components leads to high economic losses. In general, exhaust gas temperature(EGT) is used to monitor the performance of hot components.However, during the early stages of a failure, the fault information is weak, and is simultaneously affected by various types of interference, such as the complex working conditions, ambient conditions, gradual performance degradation of the compressors and turbines, and noise. Additionally, inadequate effective information of the gas turbine also restricts the establishment of the detection model. To solve the above problems, this paper proposes an anomaly detection method based on frequent pattern extraction. A frequent pattern model(FPM) is applied to indicate the inherent regularity of change in EGT occurring from different types of interference. In this study, based on a genetic algorithm and support vector machine regression, the relationship model between the EGT and interference was tentatively built. The modeling accuracy was then further improved through the selection of the kernel function and training data. Experiments indicate that the optimal kernel function is linear and that the optimal training data should be balanced in addition to covering the appropriate range of operating conditions and ambient temperature. Furthermore, the thresholds based on the Pauta criterion that is automatically obtained during the modeling process, are used to determine whether hot components are operating abnormally. Moreover, the FPM is compared with the similarity theory, which demonstrates that the FPM can better suppress the effect of the component performance degradation and fuel heat value fluctuation. Finally, the effectiveness of the proposed method is validated on seven months of actual data obtained from a Titan130 gas turbine on an offshore oil platform. The results indicate that the proposed method can sensitively detect malfunctions in hot components during the early stages of a fault, and is robust to various types of interference.