Opportunistic perfect preventive maintenance policy in presence of masked data

In this paper, the maintenance optimization problem of multi-component system is considered. It is assumed that the exact cause of system failure might be masked. That is, the exact cause of failure is unknown, and we only know that it belongs to a set called mask set. Both opportunistic perfect preventive maintenance (OPPM) and perfect corrective maintenance are considered. Threshold of OPPM and inter-inspection interval are considered as decision parameters which are optimized using long-run cost rate criteria. The applicability of the proposed maintenance policy is investigated using an illustrative example.

MODEL-ORIENTED PROGRAMMING

The problem of complex multi-component system processing arises in many fields of science and engineering. A system can be described in terms of its components, behavior, and interaction. This work proposes a new declarative Turing complete “model-oriented” programming paradigm based on the concept of “model-component” - a complex structure with well-defined characteristics and behavior, and no external methods. The set of model-components is closed under the union operation of model-components into “model-complex”. The proposed approach allows the program to describe the complex system and behavior of its components in a declarative way, possesses a higher level of encapsulation than the object-oriented paradigm, involves a reduced amount of imperative programming, and is naturally focused on parallel computations.

Modeling Simulation and Maintenance Strategy Optimization of Multi-component System

As the system tends to be complicated and refined, the probability and cost of failure also increase. In the actual operation and production, the system will be affected by various factors such as temperature, humidity and other factors to change their original working environment, which will have greater impact on the reliability of the system. Based on the reliability theory, this paper mainly studies the k-out-of-n: F system, constructs the reliability model of the system in dynamic environment, gives the simulation algorithm of the system reliability. Finally, this paper puts forward two maintenance strategies which are optimized to achieve the purpose of minimizing the long-term average cost during system operation.

Gamma Irradiation-Induced Preparation of Polyacrylonitrile Acrylamide Nano-Silica for Removal of Some Hazardous Metals

Gamma-irradiation initiated polymerization was utilized to prepare polyacrylonitrile acrylamide nano-silica {P(AN-AM)-NS}. Various analytical tools like XRD, FT-IR, SEM, TEM and DTA & TGA were used to estimate the morphology, functional groups, and structure of {P(AN-AM)-NS} nanocomposite. The ability of {P(AN-AM)-NS} nanocomposite to remove Cs(I), Pb(II), Cd(II), Sr(II), and Cu(II) ions from the multi-component system was evaluated by batch techniques considering the influence of (shaking time, pH, reaction temperatures), and capacity. At the optimum pH, distribution coefficients have selectivity order; Pb2+ > Cs+ > Cu2+ > Cd2+ > Sr2+. The kinetic data obey pseudo-second-order models. The capacity was reduced by increasing the heating temperatures of solid powder. The thermodynamic parameters showed an endothermic and spontaneous. The investigation proved that {P(AN-AM)-NS} nanocomposite is a suitable organic-inorganic sorbent for the sorption of the studied ions from liquid solutions and could be considered as potential material for purification of effluent polluted with these ions.

Maintenance modelling and optimization in the industry 4.0 context: Applications to the maintenance and design of industrial vehicles

This paper focused on the maintenance problems encountered by industrial vehicles within the Volvo Group. The main goal of the research on this subject was to propose to customers’ a personalized maintenance offer which adapts to their constraints while reducing the impact on the operating costs. To achieve this, a policy has been developed. This policy works on the dynamic gathering of information using both the available monitoring information and the knowledge of the multi-component system. Its objective is to guarantee to the customer the autonomy of its system over given periods of operation while minimizing the total cost of maintenance. The paper showed that the policy developed does indeed reduce the total maintenance cost compared to the previous policy used within the Volvo group. Nevertheless, this policy still has room for improvement.

Condition-Based Maintenance Modeling and Reliability Assessment for Multi-Component Systems with Structural Dependence under Extended Warranty

For engineering and production systems, due to the structural dependence between components, the disassembly operation caused by the replacement of components will affect the failure and degradation processes of other components in the system. In order to optimize the extended warranty (EW) cost of the multi‐component system with structural dependence, this paper described the structural dependence and modeled the disassembly operation impact, and then the failure rate model of the component considering the impact of disassembly operation under EW was developed. Combined with the actual situation, a condition-based maintenance (CBM) strategy was employed to construct the EW cost model of the multi‐component system with structural dependence. Monte Carlo simulation was proposed to determine the optimal EW cost of the system and the optimal periodic inspection interval of the CBM strategy. Finally, a numerical example of the planetary gear train of an automobile generator is introduced to demonstrate the feasibility and advantages of the proposed model in EW cost optimization and the analysis of disassembly operation impact on the optimal maintenance strategy.