In the design stage of mechanical components, the uncertainty in both the environmental load and the material parameters needs to be taken into consideration and a safety margin is required to guarantee the intrinsic reliability of mechanical components. The safety factor is comprehensively used in the practical design of mechanical components, which is industry specific and determined by the experience of engineers. However, the empirical safety factor cannot quantify the uncertainty and risk in mechanical design. Therefore, reliability analysis of mechanical products has gained more and more popularity [1] to [3].Reliability is defined as the probability that a product performs its intended functions without failure during a specified time period. For mechanical components, the load-strength interference (LSI) model is the most important analytical method in reliability assessment. The conventional LSI model is essentially a static reliability model. However, gradual failure of mechanical components commonly exists in practical engineering due to the strength degradation caused by corrosion, wear, erosion creep, etc. As pointed out by Martin, constructing reliability models considering strength degradation is an important issue for reliability estimation and further research on generalized methods for the dynamic reliability analysis of mechanical components is imperative [4].To overcome the shortcomings of conventional LSI models, reliability models based on stochastic process theory are investigated in which load and strength are modelled as two stochastic processes. Lewis [5] analysed the time-dependent behaviour of a 1-out-of-2: G redundant system by combining the LSI model with a Markov model. Geidl and Saunders [6] introduced time-dependent elements into the reliability equation to estimate the reliability. Somasundaram and Dhas [7] put forward a generalized formula to estimate the reliability of a dynamic parallel system, in which components equally shared the load. Noortwijk and Weide [8] developed a reliability model, in which load and strength are described as two stochastic processes. Labeau et al. proposed the framework of a dynamic reliability platform and identified its main constituents [9]. Zhang et al. analysed the main methods for dynamic reliability estimation of nuclear power plants, which include discrete dynamic event trees and Monte Carlo simulation [10]. Slak analysed production planning and scheduling, cutting tools and material flow process, and manufacturing capacities [11]. Barkallah et al. proposed a method for process planning to determine the tolerance for manufacturing with statistical tools [12].As a matter of fact, the reliability models based on stochastic process theory, such as the timedependent model, Markov model, etc, are the most important tools for dynamic reliability analysis. The Markov models are mainly used for dynamic reliability analysis of electronic elements and multi- Dynamic Reliability Analysis of Mechanical Components Based on Equivalent Strength Degradat...

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In this paper, dynamic reliability models of series mechanical systems in terms of stress parameters and strength parameters are established, in which strength degradation path dependence (SDPD) and failure dependence of components in the system are taken into consideration. Despite the computational convenience for reliability evaluation by using the independent strength distribution at each load application, large errors could be caused due to neglecting the existence of SDPD. In this paper, influences of SDPD on both dynamic system reliability and failure dependence are investigated. Moreover, the impacts of the dispersion of initial strength and the number of components in a system on the influences of SDPD on dynamic system reliability are analyzed. In addition, the clamp band joint system is used as illustrative examples to demonstrate the proposed models. The results show that SDPD have considerable influences, which vary in different operational stage of series mechanical systems, on dynamic system reliability and failure dependence. Besides, the dispersion of initial strength and the number of components have different impacts on the effects of SDPD on system reliability.

Oolitic hematite is an important iron ore resource. Because of its special feature，it can not be effectively separated by conventional beneficiation method. A new reduction and separation processe was used to treated an oolitic hematite in This study. The main factors influencing reduction was determined in the test. The main performance indexes of the product from this process were described as follows: iron grade>85%; metallization rate>97%; iron recovery>92%.

Considering the fuzziness of load, strength, operational states, and state probability, reliability models of multistate systems are developed based on universal generating function (UGF). The fuzzy UGF of load and the fuzzy UGF of strength are proposed in this paper, which are used to derive the fuzzy component UGF and the fuzzy system UGF. By defining the decomposition operator and the inner product operator, failure dependence and effects of multiple load applications are taken into account in the established reliability models. Moreover, dynamic fuzzy reliability models of multistate systems are constructed considering the strength degradation of components. The results show that failure dependence and the effects of multiple load applications have significant impacts on system reliability, which considerably decrease system reliability and increase the fuzziness of system reliability under low performance requirements. Besides, in the dynamic reliability analysis of multistate systems, strength degradation dependence could lead to large computational error.