Confocal probes are widely employed in many industrial fields due to the depth-sectioning effect. The author’s group has also proposed a chromatic confocal probe employing a mode-locked femtosecond laser source which can realize an axial resolution of 30 nm and a measurement range of 40 μm Efforts have also been made to improve the thermal stability of the developed femtosecond laser chromatic confocal probe so that the probe can be applied for long-term displacement measurement or surface profile measurement. Meanwhile, surface profile measurement has not been carried out by using the developed femtosecond laser chromatic confocal probe. For the verification of the performance of developed probe in profile measurement, in this paper, an experimental setup is built and a basic experiment is carried out. By using the probe with further improved thermal stability, the measurement of a sample surface profile is carried out. In this paper, the development of the experimental setup with the femtosecond laser chromatic confocal probe, as well as the results of the surface profile measurements, is presented.

Angle sensors based on the laser autocollimation are often employed to evaluate surface profiles of a target of interest. The authors have developed a femtosecond laser angle sensor, in which a spectrometer or an optical spectrum analyzer with a single-mode fiber is employed as the photodetector for simultaneous capturing of the multiple optical modes. In this paper, the concept of the femtosecond laser angle sensor is applied to evaluate the surface profile of a target of interest. An optical setup is designed in such a way that each mode in the spectrum of the mode-locked femtosecond is utilized as the laser beam to measure the local slope of a measurement target at each different point to evaluate the surface profile. Some basic experiments are carried out by using the developed optical setup with a mode-locked femtosecond laser source to evaluate basic performances of the developed optical setup as an optical angle sensor.

In order to explore the operation and maintenance characteristics of important auxiliary machines in large-scale power plant coal-fired boilers, a running state assessment model for auxiliary equipment is established. In this paper, taking the complex variability of the operating conditions of thermal power equipment into consider, auto encoder model combined with fuzzy synthetic is proposed. Based on the residual of the model results and the actual power plant operation data, combined with the fuzzy evaluation model to establish a state assessment model, and analyze the actual situation of the induced draft fan of the power plant, to make a real-time assessment of operation status. The evaluation results show the advantages of the state assessment strategy proposed in this paper, and it can reflect the deterioration of the induced draft fan status in time, providing guidance for the operation and maintenance of the equipment.

The growing resource shortage and environmental concerns have forced mankind to develop and utilize renewable energy sources. The penetration of solar photovoltaic (PV) power in the electricity market has been increasing over the past few decades due to its low construction costs, zero pollution nature, and enormous support from governments. However, the intermittency and randomness of PV power also cause huge grid fluctuations which limit its integration in the system. An accurate forecasting of solar PV power generation and optimization of operation and maintenance (O&M) management are essential for further development of the solar PV farms. A great number of related researches have been done in recent years. A review of PV power generation forecasting techniques together with their brief applications on the optimization of O&M management is presented in this paper. Machine learning methods are thought to be the most suitable at the present stage because of their ease of implementation and their capability in processing non-linear, complex data sets. Typical forecasting accuracy measures are summarized and further applications of PV power forecasting on the O&M management are also presented.

Bayesian statistics is a quintessential tool for model validation in many applications including smart materials, adaptive structures, and intelligent systems. It typically uses either experimental data or high-fidelity simulations to infer model parameter uncertainty of reduced order models due to experimental noise and homogenization of quantum or atomistic behavior. When heterogeneous data is available for Bayesian inference, open questions remain on appropriate methods to fuse data and avoid inappropriate weighting on individual data sets. To address this issue, we implement a Bayesian statistical method that begins with maximizing entropy. We show how this method can weight heterogeneous data automatically during the inference process through the error covariance. This Maximum Entropy (ME) method is demonstrated by quantifying uncertainty in 1) a ferroelectric domain structure model and 2) a finite deforming electrostrictive membrane model. The ferroelectric phase field model identifies continuum parameters from multiple density functional theory calculations. In the case of the electrostrictive membrane, parameters are estimated from both mechanical and electric displacement experimental measurements.

Corrective maintenance and preventive maintenance are two common maintenance strategies used in the wind farms, whose drawbacks are obvious and significant. Opportunistic maintenance strategy takes advantage of the dependencies existing among the wind turbine components and implement combined maintenance actions to reduce the huge downtime cost. The opportunistic maintenance strategy for wind turbines has made a great progress, as well as the strategy considering imperfect and condition-based maintenance. However, existing maintenance strategy researches are usually concerned with the maintenance itself and the effects of power generation are barely considered. Nowadays, a current research trend in manufacturing system is the integration of maintenance and production planning. In this paper, the effects of power generation on opportunistic maintenance strategy for wind turbines considering reliability are researched. The opportunistic maintenance reliability threshold is not constant and depends on the real-time power generation. Numerical examples are used to illustrate the economical advantages of this proposed strategy over traditional opportunistic maintenance strategy. Moreover, the optimal maintenance combination is also provided.

In this study, the uncertain free vibration of structures with hybrid (random and fuzzy) parameters is investigated. The non-deterministic Young’s modulus of structural elements is modeled as random variables while the uncertain mass density is represented by fuzzy parameters with associated membership functions. By implementing the α -sublevel strategy, the stochastic fuzzy eigenvalue problem is rigorously transformed into a series of stochastic interval analysis of natural frequencies. In this context, the Unified Interval Stochastic Sampling (UISS) is adopted such that the statistical characteristics of the upper and lower bounds of natural frequencies at each α -sublevel can be calculated. Subsequently, the membership functions of mean and standard deviations are respectively established for the extreme bounds of eigenvalues. Benefiting from the stochastic samples offered by the UISS method, the probability profiles of the uncertain natural frequencies at each α -sublevel can be identified by utilizing either parametric or nonparametric statistical inference methods. The accuracy and effectiveness of the proposed approach is demonstrated by analyzing a practically motivated 3D structure with comparison to the results obtained by the conventional Monte Carlo simulation method.

The Maximum Entropy (ME) method is shown to provide a new approach for quantifying model uncertainty in the presence of complex, heterogeneous data. This is important in model validation of a variety of multifunctional constitutive relations. For example, multifunctional materials contain field-coupled material parameters that should be self-consistent regardless of the measurement. A classical example is piezoelectricity which may be quantified from charge induced by stress or strain induced by an electric field. The proposed tools provide new statistical information to address measurement discrepancies, guide model development, and catalyze materials discovery for data fusion problems. The error between the model outputs and heterogeneous data is quantified and used to formulate a second moment constraint within the entropy functional. This leads to an augmented likelihood function that weights each individual set of data by its respective variance and covariance between each data set. As a first step, the method is evaluated on a piezoelectric ceramic to illustrate how the covariance matrix influences piezoelectric parameter estimation from heterogeneous electric displacement and strain data.

Seismic risk of nuclear power plant has drawn increasing attention after Fukushima accident. An intensive study has been carried out in this paper, including sampling of component and structure fragility based on Monte Carlo method, fragility analysis on system or plant level, convolution of seismic hazard curves and fragility curves. To derive more accurate quantification results, the binary decision diagram (BDD) algorithm was introduced into the quantification process, which effectively reduces the deficiency of the conventional method on coping with large probability events and negated logic. Seismic Probabilistic Safety Analysis (PSA/PRA) quantification software was developed based on algorithms discussed in this paper. Tests and application has been made for this software with a specific nuclear power plant seismic PSA model. The results show that this software is effective on seismic PSA quantification.

Zero-suppressed Binary Decision Diagram (ZBDD) algorithm is an advanced method in fault tree analysis, which is developing quickly in recent years and being used in the development of the Probabilistic Safety Assessment (PSA) Quantification Engine. This algorithm converts a fault tree to a ZBDD structure, solves the minimal cut sets and calculates the top node unavailability. The ordering of the basic events and logical gates is the core technique of the ZBDD algorithm, which determines the efficiency of the ZBDD conversion and the size of the ZBDD structure. A variable ordering method based on the structure of the fault tree is developed in this paper, which gives a better basic events order by compressing the fault tree; meanwhile, the method offers a logical gates order. The nodes order derived from this method can accelerate the ZBDD conversion obviously.

Minimal Cut-sets (MCS) post processing is an important part of the Probabilistic Safety Assessment (PSA) analysis. Two alternative post processing methods have been described in this paper based on traditional and Zero-suppressed Binary Decision Diagram (ZBDD) methods. The first post processing method, named the Quick MCS Post Processing Method, obtains MCS results directly from the cut-sets list using a quick sort method according to a certain set of sorting rules. The alternative post processing method, named the ZBDD Based MCS Post Processing Method, obtains MCS results from the ZBDD structure which encodes the MCS, using the ZBDD algorithm according to the post processing rules. Tests show that both of the two methods can derive the accurate MCS post processing results.

Nowadays, the management level and information construction of wind power industry are still relatively backward, for example, the existing maintenance models for wind farm are much too single, and corrective maintenance strategy is the most commonly used, which means that maintenance measures are initiated only after a breakdown occurs in the system. Moreover, the wind farm spare parts management is out-dated, no practical and accurate spares demand assessment method is available. In order to enrich the choices of maintenance methods and eliminate the subjective influence in the demand analysis of spare parts, a spare parts demand prediction method for wind farm based on periodic maintenance strategy considering combination of different maintenance models for wind farms is proposed in this paper, which consists of five major steps, acquire the reliability functions of components, establish the maintenance strategy, set the maintenance parameters, maintenance strategy simulation and spare parts demand prediction. The discrete event simulation method is used to solve the prediction model, and results demonstrate the operability and practicality of the proposed demand forecasting method, which can provide guidance for the actual operation and maintenance of wind farms.

Studies show that fusion modeling can improve the forecasting accuracy of wind power. Fusion modeling is the process of selective use of information from individual forecasting models. The reasonable evaluation of the individual models is the premise and basis of model optimization so that the individual models with high forecasting accuracy can be selected to establish the fusion model. Because the results of a single index model evaluation may not be comprehensive, the multi-index fusion evaluation method based on maximizing deviations and subjective correction is proposed. The method is applied to the selection of short-term wind power forecasting models. Firstly, this method establishes the individual model base of wind power forecasting model. Secondly, it establishes the more comprehensive evaluation index system. Thirdly, it combines maximizing deviations with the subjective correction coefficient to determine the comprehensive weight of each model, which is used to calculate the fusion evaluation value and get the evaluation order to achieve the model optimization. Finally, based on five years of data from a wind power plant in Shanxi Province, the validated experiments by multiple sets of forecasting data have been done using MATLAB in this paper. The simulation results demonstrate that the evaluation based on the proposed fusion evaluation method is more comprehensive and stable compared to evaluation using a single index. More importantly, it can effectively guide the model optimization with simple operating steps.

With the demand of renewable energy due to the pressure from environmental pollution and global warming, the wind industry has been growing rapidly over the past decades during which the offshore wind innovation is becoming more and more attractive because of vast offshore wind resources. In this paper, a novel tapered-column semi-submersible floating foundation with economical mooring system is developed to support 6MW wind turbine in South China Sea. The coupled aero-hydro-servo-structural analysis is done in GH-Bladed software to obtain the global dynamic response of the whole floating wind turbine. By combining the derived wind loads with the wave and current induced loads, the global performances of the foundation and attached mooring system under both extreme conditions and normal operation conditions are analyzed in AQWA software. The result reveals that the floating foundation complies with the design standard and meets requirements of wind turbine. This novel patent-pending floating foundation with tapered columns is proved as a successful design with high material efficiency and good seakeeping performance. Also a reliable and efficient design methodology of floating foundation based on optimal cost is provided in this paper which can be used as design reference of floating foundations.