摘要：Dry cohesive granular flow can show pronounced nonlinear behaviour, which needs to be identified prior to the design of bulk solids handling systems. Laboratory tests yield time series data representing the flow behaviour of granular systems, but widely accepted analytical procedures for these systems have not been established yet. In this paper, the premise that avalanching flow yields nonlinear time series that can be identified by surrogate data methods is investigated. Preliminary results indicate that powder systems with a high propensity for avalanching yield nonlinear data and conversely, powder systems exhibiting good flow properties tend to yield linear time series data that can be identified by amplitude adjusted Fourier transform surrogate data methods.
关键词：KeywordsDynamic Process MonitoringConvolutional Neural NetworksGranular FlowAvalanching Powder Flow
