Vibrating machines such as screens and feeders are used for classification and conveying of bulk material in various industries such as mining, steel and cement.
In correlation with the high-value throughput of the target processes, there are numerous valuable information to support the efficient operation of these assets. The valuable information includes performance parameters (total load, distribution of bulk material, speed, stroke, etc.) as well as condition parameters to enable a cost-saving preventive maintenance strategy. Yet, as per known industry standards, this type of information is latent in most cases leading to significant unused cost-saving opportunities. As empirically known today, most performances as well as condition parameters do actually correlate with the machines motion pattern. State-of-the-art accelerometers can be used to capture the motion of a vibrating machine.
CITATION:Schfer, J, 2019. AI algorithms to monitor the performance and condition of vibrating machines, in Proceedings Iron Ore 2019, pp 239242 (The Australasian Institute of Mining and Metallurgy: Melbourne).