Rapid developments of wind industry arise the issue of heavy monitoring tasks. The residual monitoring based on normal behaviour modelling is a highly recommended method when fault record information is missing. However, it is difficult to achieve efficient normal behaviour modelling and dynamic residual monitoring simultaneously. To this end, a novel adaptive fault detection scheme, which merges random forest (RF) with adaptive cumulative sum (CUSUM), is proposed. The authors exploit RF to explore the non-linear mechanism between features and the target variable robustly, and obtain the residuals quickly. Then, they design the adaptive CUSUM control chart of time-varying shift to sensitively detect the changes of residuals. For illustration, they apply the proposed scheme to the supervisory control and data acquisition data acquired from a wind farm in China. The empirical results demonstrate that the proposed scheme is superior to several competing methods in capturing faults and reducing false alarms. Meanwhile, the authors find it can detect anomaly quickly, automatically and robustly under different signal-to-noise ratios. These provide operators sufficient time to adopt an effective maintenance strategy.

No abstract

Because the signal of water pump bearing is seriously disturbed by noise and the fault evolution is complex, it is difficult to describe the performance degradation trend of water pump bearing in a timely and accurate manner using the traditional performance degradation index (PDI). In this paper, a new Cluster Migration Distance (CMD) algorithm is proposed. The extraction of the indicator includes the following four steps: First, the relevant blind separation is used to extract the useful signal of the monitored bearing from the mixed signal; secondly, the impact component is further enhanced by wavelet packet analysis. Then, the redundancy of the original feature vectors is eliminated using our previously proposed KJADE (Kernel Joint Approximate Diagonalization of Eigen-matrices) method. Finally, the newly proposed CMD index is computed as PDI. By calculating the offset trajectory of the feature cluster centroid in the continuous running process of the bearing, CMD can aptly deal with the complex and variable features in the fault evolution process of the water pump bearing. The whole-life monitoring data of a 220 KW water pump system are processed. The results show that the proposed CMD index has better early-warning ability and monotonicity than the traditional kurtosis index.

A novel method of the micro device mould fabrication is reported, which combine the femtosecond two-photon polymerization and micro electroforming. As example of the results, a 2×2 micro lens array (MLA) with diameter of 10ȝP and a micro gear are fabricated using S-3 photo resist by twophoton polymerization, which use an alternative annular scanning mode with continuous variable layer space after parameter optimization to achieve good surface appearance. Then the MLA and the micro gear are electroformed to obtain nickel mould. Compared to the conventional method, this work provides an alternative, fast and effective processing method for the fabrication of micro device mould that requires arbitrary shape with high surface quality and small scale.

Professor Chin-Sheng Chen, Major ProfessorThis research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions.The first phase of the study is focused on a formal definition of the problem.Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver.The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot v scheduling. A range of simple sequencing rules are identified for each of the three subproblems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters.For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the DixonSilver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.