This paper is focused on developing a compensation module for reducing the thermal errors of a computer numerical control (CNC) milling machine. The thermal induced displacement variations of machine tools are a vital problem that causes positioning errors to be over than 65%. To achieve a high accuracy of machine tools, it is important to find the effective methods for reducing the thermal errors. To this end, this study first used 14 temperature sensors to examine the real temperature fields around the machine, from which four points with high sensitivity to temperature rise were selected as the major locations for creating the representative thermal model. With the model, the compensation system for controlling the displacement variation was developed. The proposed model has been applied to the milling machine. Current results show that the displacement variations on the x- and y-axes and the position error at the tool center were controlled within 20 µm when the compensation system was activated. The feasibility of the compensation system was successfully demonstrated in application on the milling operation.

Wind energy is becoming a common source of renewable energy in the world. Wind turbines are increasing in number, both for onshore and offshore applications. One challenge with wind turbines is in detecting anomalies that cause their breakdown. Due to the complex nature of the wind turbine assembly, it is quite an extensive process to detect causes of malfunctions in the system. This study uses the Mahalanobis distance (MD) to detect anomalies in wind turbine operation, using SCADA alarm data as a comparison. Different predictive models were generated as the bases for analyses in MD computations. Using the SCADA alarm data as a reference, trend patterns that deviated from the threshold value were compared. Results showed that the MD could be used to detect anomalies within a group of data sets, with behaviors learned based on the model used. A large portion of those data sets deviated from the threshold level, corresponding to serious alarms in the SCADA data. We concluded that the MD can detect anomalies in different wind turbine components, based on this study. MD analysis of models can be used in conditions monitoring systems of wind turbines.

In order to develop dynamic analysis technologies regarding the design of offshore wind turbine generators (OWTGs), a special project called Offshore Code Comparison Collaboration Continuation (OC4) was conducted by IEA (International Energy Agency) in 2010. A similar project named INER-OC4 has been performed by the Institute of Nuclear Energy Research (INER) to develop the OWTG technologies of Taiwan. Since the jacket substructure will be applied to Taiwan OWTGs before 2020, the INER-OC4 project has been devoted to the design and analysis of jacket support structure. In this work, the preliminary result of INER-OC4 is presented. A simplified analysis procedure for jacket support structure has been proposed. Both of the NREL (National Renewable Energy Laboratory) 5 MW OWTG FAST model and OC4 jacket substructure model have been built and analyzed under severe design load cases (DLCs) of IEC (International Electrotechnical commission) 61400-3. Simulation results of six severe DLCs are performed in this work and the results are in agreement with the requirements of API (American Petroleum Institute) and NORSOK (Norwegian Petroleum Industry) standards.

For improving the defects in milling processes caused by traditional spindle bearings, e.g., the dimensional discrepancy of a finished workpiece due to bearing wear or oil pollution by lubricant, a novel embedded cylindrical-array magnetic actuator (ECAMA) is designed for milling applications. Since ECAMA is a non-contact type actuator, a control strategy named fuzzy model-reference adaptive control (FMRAC) is synthesized to account for the nonlinearities of milling dynamics and magnetic force. In order to ensure the superior performance of spindle position regulation, the employed models in FMRAC are all constructed by experiments. Based on the experimental results, the magnetic force by ECAMA is much stronger than that by the traditional active magnetic bearing (AMB) design under the same test conditions and identical overall size. The efficacy of ECAMA to suppress the spindle position deviation with the aid of FMRAC has been verified as well via numerical simulations and practical metal cutting.

Spherical porous air bearing (SPAB) systems have been extensively used in various mechanical engineering applications. SPABs are promising materials in high-rotational speed, high-precision, and highstiffness instruments. In SPAB systems, a rotor is supported by gas bearings, which provides higher rotational speed and lower heat generation environment than oil bearings do. Furthermore, SPAB does not cause deformation. Although, the supporting force of gas bearings is less, their stability is better than that of oil films. However, because the pressure distribution in the gas films is nonlinear, they are prone to failure at specific critical speeds, rotor imbalances, or inappropriate operations, which results in nonperiodic or chaotic motion and causes structural fatigue to the system. To understand and control the operating conditions of the SPAB systems during the nonperiodic motion, first, the governing equations of the SPAB system were solved to obtain the dynamic behavior of the rotor center. Then, the performance of the SPAB system were examined under different operating conditions by generating the maximum Lyapunov exponents (MLEs). However, the calculation process of MLE is extremely time consuming and complex. To solve this problem efficiently, a high-precision machine learning (ML)–based MLE prediction model was proposed in this study. The results show that the training process can be finished within few minutes, and the prediction process is able to be completed within few seconds. Meanwhile, the results demonstrate the merit of using the machine learning method for solving the MLE prediction problem and shorten the calculation time significantly. The proposed prediction model achieves excellent prediction outcome and it is more efficient and precise than traditional iteration scheme for the calculation of MLE. The feasibility of the proposed model is validated and the results also are the major contribution of this study.