Lu Ningyun

Data-based incipient fault detection and prediction for satellite's attitude control system

Incipient fault detection (FD) and prediction are crucial for the safe operation of in-orbit satellites attitude control system (ACS). In this paper, a locally linear embedding (LLE) model combined with exponentially weighted moving average (EWMA) technique is proposed in FD for ACS, which is more suitable when the magnitude of the fault is small. After that, fault trend prediction with multi variables is conducted. Firstly, a preprocessing for high-dimensional telemetry data from the satellite ACS is conducted. Considering that there exists non-linear correlation relationship among telemetry parameters in ACS, LLE is used for online FD, while EWMA is used to accumulate the fault value. Based on the results of fault detection, vector autoregressive integrated moving average model (VARMA) is used for tracking the trend of fault. The case study on a simulated satellite ACS demonstrates the effectiveness of the proposed method.

A test sequencing optimization method for fault diagnosis of engineering systems

In order to solve the test sequencing problem in fault diagnosis, an optimal test point selection and test sequencing optimization algorithm are proposed. Based on the improved multi-signal model and using the information entropy algorithm as the heuristic function, a test sequencing optimization algorithm with one step backtracking is proposed and validated on the multi-signal model of the hydraulic AGC system. The example shows that the algorithm can isolate the fault states with less test cost.

A closed-loop multivariable quality control scheme for injection molding process

Closed-loop quality control by adjusting multiple process parameters can enlarge the operating space; therefore, it can deal with more severe quality deviations in the quality control system. A closed-loop multivariable quality control scheme is presented for injection molding process. The initial process settings are determined by DOE based parameter optimization. The multilayer group method of data handling (GMDH) algorithm is used to develop the quality model. The sequential quadratic programming (SQP) method is adopted in the quality controller design, where the optimal parameters are calculated cycle by cycle according to the observed quality deviation. Quality measurements are provided by a multiway partial least square (MPLS) regression model. The illustrative example can show the feasibility of the proposed quality control scheme.