Modeling on Spare Parts Inventory Control Under Condition Based Maintenance Strategy

In order to optimize the spare parts inventory, we present a decision-making model under condition based maintenance policy for a single equipment system subjected to continuous and random deterioration. Firstly,a probability model of the spare parts support is established, according to the requirement of a predetermined probability of stockout. It can determine the optimal spare parts stock level. Secondly, the spare parts ordering decision is made according to the equipment deterioration level, and it can optimize the spare parts ordering. The objectives of this model are to minimize the spare parts inventory, and the expected total operating cost. Thirdly,a numerical example is given to illustrate this model. The results prove that the optimal preventive maintenance threshold obtained from the proposed model can satisfy the spare parts support requirements.

Early warning for geo-hazards based on the weather condition in China

According to national early warning practice for geo-hazards from 2003 to 2005,it is systematically concluded that the basic characteristics of geo-hazards,early warning method and forecast result based on the geological maps of China in a scale 1∶6 000 000.With the contrast of different characters between sustained rainfall and typhoon rainfall inducing geo-hazards,the disaster reduction result and some problems are preliminarily analyzed.Some basic recognition is that early warning to geo-hazards is feasible,national scale forecast is only to call attention,but can't immediately be used to disaster reduction decision-making.And,the future direction is to build a united disaster reduction framework of early warning system including national,provincial and county levels based on weather factors in different scale of area.

A NEW FORMULATION OF BERNSTEIN-BEZIER BASED SMOOTHNESS CONDITIONS FOR pp FUNCTIONS

In this note, we establish a new formulation of smoothness conditions for piecewise polynomial (: =pp) functions in terms of the B-net representation in the general n-dimensional setting. It plays an important role for 2-dimensional setting in the constructive proof of the fact that the spaces of polynomial splines with smoothness rand total degree k≥3r+2 over arbitrary triangulations achieve the optimal approximation order with the approximation constant depending only on k and the smallest angle of the partition in [5].

Remaining useful life estimation based on Wiener degradation processes with random failure threshold

Remaining useful life(RUL) estimation based on condition monitoring data is central to condition based maintenance(CBM). In the current methods about the Wiener process based RUL estimation, the randomness of the failure threshold has not been studied thoroughly. In this work, by using the truncated normal distribution to model random failure threshold(RFT), an analytical and closed-form RUL distribution based on the current observed data was derived considering the posterior distribution of the drift parameter. Then, the Bayesian method was used to update the prior estimation of failure threshold. To solve the uncertainty of the censored in situ data of failure threshold, the expectation maximization(EM) algorithm is used to calculate the posteriori estimation of failure threshold. Numerical examples show that considering the randomness of the failure threshold and updating the prior information of RFT could improve the accuracy of real time RUL estimation.

Prediction Approach to Life on Wing for Civil Aeroengine

To reduce engine maintenance cost and support safe operation, a prediction method of engine life on wing was proposed. This method is a kind of regression model which is a function of the condition monitoring and failure data. Key causes of engine removals were analyzed, and the life limit due to performance deterioration was predicted by proportional hazards model. Then the scheduled removal causes were considered as constraints of engine life to predicte the finai life on wing. Application of the proposed prediction method to the case of CF6-80C2A5 engine fleet in an airline proved its effectiveness.

Machinery and Equipment Maintenance Strategy and Choice

The advantages, disadvantages and characteristics of various maintenance strategies for modern mechanical equipment are analyzed. Combined with the system structure and functional characteristics of engineering equipment,it puts forward the selection method of maintenance strategies for different types of equipment and failure modes. The view of this article is that the comprehensive maintenance strategy, which is based on condition based maintenance(CBM) and combines various maintenance strategies. This will become the main development direction of engineering equipment maintenance.

Road surface condition sensor based on scanning detection of backward power

A method of detecting dry, icy and wet road surface conditions based on scanniag detection of single wavelength backward power is proposed in this letter. The detector is used to receive the backward scattered power which changes with the incidence angle. The relationship between backward power and incidence angle is used to find out the effective angle range and distinguish method. Experiment and simulation show that it is feasible to classifv these three conditions within incidence angle of 5.3 degree.

Study on the Methods of Equipment Maintenance Management

This thesis discusses the importance of equipment maintenance management, then introduces the development tendency of equipment maintenance management. In the end it analyses and studies the design of a computerized maintenance management system, which offers an integration technique.

Burnett Order Stress and Spatially-Dependent Boundary Conditions for the Lattice Boltzmann Method

Stress boundary conditions for the lattice Boltzmann equation that are consistent to Burnett order are proposed and imposed using a moment-based method.The accuracy of the method with complicated spatially-dependent boundary conditions for stress and velocity is investigated using the regularized lid-driven cavity flow.The complete set of boundary conditions,which involve gradients evaluated at the boundaries,are implemented locally.A recently-derived collision operator with modified equilibria and velocity-dependent collision rates to reduce the defect in Galilean invariance is also investigated.Numerical results are in excellent agreement with existing benchmark data and exhibit second-order convergence.The lattice Boltzmann stress field is studied and shown to depart significantly from the Newtonian viscous stress when the ratio of Mach to Reynolds numbers is not negligibly small.

measurement weak repeatability covariant condition wave-packet collapse

The conflict between the dynamics postulate（unitary evolution） and the measurement postulate（wavepacket collapse） of quantum mechanics has been reconciled by Zurek from an information transfer perspective [Phys.Rev. A 76（2007） 052110], and has further been extended to a more general scenario [Phys. Rev. A 87（2013） 052111].In this paper, we reconsider Zurek＇s new derivation by using weak repeatability postulate or covariant condition instead of repeatability postulate.

Progress and trend of sensor technology for on-line oil monitoring

Oil monitoring constitutes an important and essential component of condition monitoring technologies and has distinguished advantages in revealing wear,lubrication and friction conditions of tribo-pairs.Newly developed on-line/in-line oil monitoring technologies extend the merits into real-time applications and demonstrate significant benefits in maintenance and management of equipment.This paper comprehensively reviews the progress of on-line/in-line oil monitoring techniques including sensor technologies,their scopes and industrial applications.Based on the existing developments and applications of the sensors for oil quality and wear debris measurements,the trends for future sensor developments are discussed with focuses on accurate,integrated and intelligent features along with exploring a fundamental issue,that is,acquiring the knowledge on degradation mechanisms which has not received sufficient attention until now.Current status of applications of on-line oil monitoring is also reviewed.Although limited reports have been found on this topic,increasing awareness and encouraging progress in on-line monitoring techniques are recognized in applications such as aircraft,shipping,railway,mining,etc.Key fundamental issues for further extending the on-line oil monitoring techniques in industries are proposed and they include long-term reliability of sensors in harsh conditions,and agreement with fault or maintenance determination.

An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

An accurate estimation of the remaining useful life（RUL） not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance（CBM）. For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work,an adaptive-order particle filter（AOPF） prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump＇s degradation.With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method.

DATA DRIVEN MODELING FOR POWER TRANSFORMER LIFESPAN EVALUATION

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation＇s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit （RTU） system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Developing a predictive maintenance model for vessel machinery

The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during operations.The objective of this study is to initiate the development of a predictive maintenance solution in the shipping industry based on a computational artificial intelligence model using real-time monitoring data.The data analysed originates from the historical values from sensors measuring the vessel´s engines and compressors health and the software used to analyse these data was R.The results demonstrated key parameters held a stronger influence in the overall state of the components and proved in most cases strong correlations amongst sensor data from the same equipment.The results also showed a great potential to serve as inputs for developing a predictive model,yet further elements including failure modes identification,detection of potential failures and asset criticality are some of the issues required to define prior designing the algorithms and a solution based on artificial intelligence.A systematic approach using big data and machine learning as techniques to create predictive maintenance strategies is already creating disruption within the shipping industry,and maritime organizations need to consider how to implement these new technologies into their business operations and to improve the speed and accuracy in their maintenance decision making.