Several of these (equipment related, mode changes, and thermal considerations) involve the process design of equipment.
Fuzzy reasoning algorithms were designed to evaluate and assess the likelihood of equipment failure mode precipitation and aggravation.
The equipment assembly modes are presented in Table 3.
Designers must be aware of the maintenance strategy and of the possible equipment failure modes.
The topography and current measurements were carried out with a commercial AFM equipment (Multi-Mode V, Bruker) at room temperature.
Furthermore, an approximate reasoning scheme which considers local, product, and adjacent machinery effects was constructed to prioritize the equipment failure modes likely to precipitate in the process.
The active elements are determined by the design and passive elements are governed by the maintenance philosophy and designers have to be aware of the maintenance strategy and of the possible equipment failure modes.
Techniques to rapidly model local spaces, using 3D range data, can enable implementation of: (1) real-time obstacle avoidance for improved safety, (2) advanced automated equipment control modes, and (3) as-built data acquisition for improved quantity tracking, engineering, and project control systems.
Table 2 Influence of equipment suspension style on carbody modes Modal name Modal frequency (Hz) Elastic Rigid LF vertical bending 10.15 – LF lateral bending 10.42 – Rhombic 14.59 14.68 HF vertical bending 16.82 12.59 HF lateral bending 17.61 13.87 1st breath 19.99 21.11 Torsion 24.98 25.68 2nd vertical bending 30.29 30.27 LF Low frequency, HF High frequency.
In addition, if the transmission techniques and the way in which resources are allocated are improved, a noticeable enhancement will be obtained for the access equipment during active mode, that complements the already important reductions product of sleep modes.
