An integrated framework for intelligent reliability design and prognostic health management of space robotic systems

Space robotics has received significant attention from both theoretic research and applications. The mission in future will be involving and be heavily supported by different robotic systems, such as planetary rovers and manipulators for orbital servicing, etc. The harsh environment in space can severely affect the operating safety of space robotic systems and therefore the lifecycle reliability problem and prognostic healthmanagement have paramount importance to make the space robotic systems more successful and safer in future space missions. Though there has a great deal of research on failure detection, fault diagnosis and condition monitoring for conventional space systems and other engineering applications such as nuclear power station, it has a lack of research on the general methodology for both the reliability design and health management of space robotic systems to improve the operating safety. This paper proposes an integrated framework (named as iRPHM) in which the higher reliability is designed for space robotic systems by taking advantage of reliability-based intelligent design optimization while considering the expected random loadings. The prognostic health management (PHM) is implemented in the proposed framework to decrease the failures arising from the unexpected events in harsh space environment

Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm

In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations

Reliability analysis of CRAC system of a modern Data centre via Kriging algorithm

In the modern era of digital growth, Data centres serve as the important infrastructure of the interconnected global network. The Data centres perform as central hubs where immense volumes of data are processed, stored, and distributed. Reliable performance of data centres and Computer Room Air-Conditioning (CRAC) system is of critical importance, through which optimal environmental conditions are achieved. This paper proposes an interesting approach to evaluate the CRAC system’s ability to function properly under various uncertainties. Heat transfer model is developed using various environmental parameters, data server rack parameters, and evolving capacity of data servers. The uncertainties are modelled as random variables. One of the challenging issues in estimating the reliability of a system under uncertainties is to reduce the computational requirements while maintaining the accuracy. To overcome the issue, Kriging model is developed using adaptive sampling. The proposed approach is compared with the available approaches. The method shows better computational efficiency and accuracy

Robust design for the lower extremity exoskeleton under a stochastic terrain by mimicking wolf pack behaviors

While kinematics analysis plays an important role in studying human limb motions, existing methods (namely, direct and inverse kinematics) have their deficiencies. To improve, this paper develops a robust design method using artificial intelligence and applies it to the lower extremity exoskeleton design under a stochastic terrain. An inverse kinematic model is first built considering the impact on human's comfort from the stochastic terrain. Then, a robust design model is constructed based on the inverse kinematic model, where the design framework mimics wolf pack behaviors and the robust design problem is thus solved for keeping probabilistic consistency between the exoskeleton and its wearer. A case study validates the effectiveness of the developed robust method and algorithm, which ensures walking comfort under the stochastic terrain within the validity of simulations

Carbon nanocages with nanographene shell for high-rate lithium ion batteries

Carbon nanocages with a nanographene shell have been prepared by catalytic decomposition of p-xylene on a MgO supported Co and Mo catalyst in supercritical CO2 at a pressure of 10.34 MPa and temperatures ranging from 650 to 750 °C. The electrochemical performance of these carbon nanocages as anodes for lithium ion batteries has been evaluated by galvanostatic cycling. The carbon nanocages prepared at a temperature of 750 °C exhibited relatively high reversible capacities, superior rate performance and excellent cycling life. The advanced performance of the carbon nanocages prepared at 750 °C is ascribed to their unique structural features: (1) nanographene shells and the good inter-cage contact ensuring fast electron transportation, (2) a porous network formed by fine pores in the carbon shell and the void space among the cages facilitating the penetration of the electrolyte and ions within the electrode, (3) thin carbon shells shortening the diffusion distance of Li ions, and (4) the high specific surface area providing a large number of active sites for charge-transfer reactions. These carbon nanocages are promising candidates for application in lithium ion batteries

Linking component importance to optimisation of preventive maintenance policy

In reliability engineering, time on performing preventive maintenance (PM) on a component in a system may affect system availability if system operation needs stopping for PM. To avoid such an availability reduction, one may adopt the following method: if a component fails, PM is carried out on a number of the other components while the failed component is being repaired. This ensures PM does not take system’s operating time. However, this raises a question: Which components should be selected for PM? This paper introduces an importance measure, called Component Maintenance Priority (CMP), which is used to select components for PM. The paper then compares the CMP with other importance measures and studies the properties of the CMP. Numerical examples are given to show the validity of the CMP

A supercritical-fluid method for growing carbon nanotubes

Large‐scale generation of multiwalled carbon nanotubes (MCNTs) is efficiently achieved through a supercritical fluid technique employing carbon dioxide as the carbon source. Nanotubes with diameters ranging from 10 to 20 nm and lengths of several tens of micrometers are synthesized (see figure). The supercritical‐fluid‐grown nanotubes also exhibit field‐emission characteristics similar to MCNTs grown by chemical‐vapor deposition

Reliability-Based Lifecycle Optimization with Maintenance Consideration

In traditional reliability-based design optimization (RBDO), a cost-type objective function is minimized while reliability constraints are maintained. The reliability constraints are usually static without the consideration of product lifecycle. In this work, several reliability-based design optimization models with different maintenance strategies are discussed. The product lifecycle cost is minimized while the constraints on product lifecycle reliability or availability are maintained. The First Order Reliability Method (FORM) is employed to calculate the time dependent reliability. An engineering example is used to demonstrate the proposed methods

Optimal Design Accounting for Reliability, Maintenance, and Warranty

Reliability-based design (RBD) ensures high reliability with a reduced cost. Most of the RBD methodologies do not account for maintenance and warranty actions. As a result, the RBD result may not be truly optimal in terms of lifecycle reliability. This work attempts to integrate reliability, maintenance, and warranty during RBD. Three RBD models are built. The total cost of production, maintenance, and warranty are minimized. The computational procedures for solving the RBD models are developed. As demonstrated by two examples, the proposed RBD models meet not only the initial reliability requirement but also the maintenance and warranty requirements with reduced costs. Copyright © 2010 by ASME

Reliability - Based Design Incorporating Several Maintenance Policies

Traditional reliability-based design optimization (RBDO) minimizes a cost-type objective function subject to reliability constraints. The reliability constraints are based on physical models, such as finite element simulation, which are used to specify the state of a component or a system. Hence the reliability is the so-called physical reliability. The reliability constraints are usually static without accounting for product lifecycle issues. In this work, several reliability-based design optimization models incorporating several maintenance policies are proposed. The product lifecycle cost is minimized while the constraints of product lifecycle reliability or availability are satisfied. The First Order Reliability Method (FORM) is employed to calculate the time dependent reliability. An engineering example is used to illustrate the proposed models