To meet the challenge of dramatically market, manufacturing process requires the high production efficiency and the stability of the machining accuracy. Thus availability is the one of crucial measurements of complex equipments. This study addresses the problem of evaluating the performance of equipment which subject to both wear-out failures and random failures. Moreover, the different degradation states of the equipment are analyzed and then, the model of the states is established using generalized stochastic Petri nets and the analytical method for assessing the availability is also built up. Finally, the availabilities under different maintenance strategies are compared and the effectiveness and efficiency of the method is verified.
Manufacturing systems are subject to unscheduled downtime due to machine malfunction. A threshold or an alarm is usually obtained based on equipment lifetime distribution to trigger maintenance work orders. The gap between conventional method and industrial practice is that the threshold is fixed and does not consider the status change due to machine degradation. In addition, the uncertainty involved in threshold calculation has not been carefully considered. An approach to extract dynamic maintenance threshold is presented for the continuously monitored degrading system considering system uncertainty. A modified two-stage degradation modeling approach is proposed to derive the degradation path for reliability prediction. A numerical case is exploited to demonstrate the benefits of the proposed approach. Results indicate that unnecessary or excessive maintenance is effectively avoided, and the machine availability will be consequently increased.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.