Parametric time-domain methods for non-stationary random vibration modelling and analysis — A critical survey and comparison

Comparative review of methods for stability monitoring in electrical power systems and vibrating structures

“…These methods do not impose a particular structure upon the evolution of the time-varying model parameters [78].…”

“…These methods impose a stochastic structure upon the evolution of the time-varying model parameters via stochastic smoothness constraints and are also referred to as Smoothness Priors TARMA (SP-TARMA) models [78]. These methods have been mainly reported for the modelling of earthquake ground motions [83], [84].…”

“…They are reviewed extensively in [78] hence only a brief summary is provided here. Following [78], they can be further divided with reference to the structure of evolution of the time-varying model parameters.…”

“…Following [78], they can be further divided with reference to the structure of evolution of the time-varying model parameters. The hierarchical tree in Figure 3 therefore has three further branches, as outlined in the next three sub-sub-sections.…”

Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application

“…A potential solution is to consider adaptive thresholds [19,24], and use apriori knowledge, either qualitative [33,62,31,23] or quantitative [53,25,47], to derive non-constant thresholds to take the time-varying uncertainties and disturbances into account. Furthermore since the residual depicted in Figure 1 is non-periodic, diagnosis approaches for machines working in non-stationary operating conditions [16,52,43,51] are not applicable. This paper instead proposes an adaptive statistical residual evaluation method, which exploits quantitative a-priori knowledge in the form of data.…”