The influence of repeated loading, residual stresses and shakedown on the behaviour of tribological contacts

Most tribological pairs carry their service load not just once but for a very large number of repeated cycles. During the early stages of this life, protective residual stresses may be developed in the near surface layers which enable loads which are of sufficient magnitude to cause initial plastic deformation to be accommodated purely elastically in the longer term. This is an example of the phenomenon of 'shakedown' and when its effects are incorporated into the design and operation schedule of machine components this process can lead to significant increases in specific loading duties or improvements in material utilization. Although the underlying principles can be demonstrated by reference to relatively simple stress systems, when a moving Hertzian pressure distribution in considered, which is the form of loading applicable to many contact problems, the situation is more complex. In the absence of exact solutions, bounding theorems, adopted from the theory of plasticity, can be used to generate appropriate load or shakedown limits so that shakedown maps can be drawn which delineate the boundaries between potentially safe and unsafe operating conditions. When the operating point of the contact lies outside the shakedown limit there will be an increment of plastic strain with each application of the load - these can accumulate leading eventually to either component failure or the loss of material by wear. © 2005 Elsevier Ltd. All rights reserved.

Structured precision modelling with Cholesky basis superposition for speech recognition

Structured precision modelling is an important approach to improve the intra-frame correlation modelling of the standard HMM, where Gaussian mixture model with diagonal covariance are used. Previous work has all been focused on direct structured representation of the precision matrices. In this paper, a new framework is proposed, where the structure of the Cholesky square root of the precision matrix is investigated, referred to as Cholesky Basis Superposition (CBS). Each Cholesky matrix associated with a particular Gaussian distribution is represented as a linear combination of a set of Gaussian independent basis upper-triangular matrices. Efficient optimization methods are derived for both combination weights and basis matrices. Experiments on a Chinese dictation task showed that the proposed approach can significantly outperformed the direct structured precision modelling with similar number of parameters as well as full covariance modelling. © 2011 IEEE.