Conveyor Protection and Reliability

Preparing bitumen ore from the mine for separation in primary extraction requires crushing and sizing the ore to an optimal size for mixing with hot process water to form a bitumen slurry, which is then pumped the primary extraction plant. To transport the bitumen ore between the crushing, sizing, and slurry mixing processes, larger conveyor systems are utilized. These conveyors rely on belts, pulleys, motors and gearboxes to provide the movement of the ore, and each component is subject to dynamic loading, impact forces, and other degradation which decreases the asset's lifespan. Unplanned conveyor downtime can result in huge production losses as well as safety concerns.

Emerson AMS technologies provide vibration and stress wave analysis utilizing PeakVue on conveyors whereas standard vibration is of little value due to high background noise and slow speed. In addition, embedded temperature sensors allow users to track material build-up on bearing housings.

The AMS 6500 Machinery Health Monitor is designed for process automation and protection system upgrade projects, and combines prediction and protection in a single chassis for rotating and reciprocating assets. The AMS 6500 is fully compliant with API 670 and integrates protection, prediction, real-time performance monitoring, and process automation by preventing missed trips. Emerson’s PlantWeb digital architecture provides enterprise-wide information needed for real-time decision making.

Emerson's AMS 9420 Wireless Vibration Transmitter delivers vibration information over a highly-reliable, self-organizing wireless network for use by operations and maintenance personnel. Configuration, diagnostics, and alerts are imported into AMS Suite: Intelligent Device Manager. The AMS 9420 is ideal for vibration monitoring applications, especially in hard-to-reach or cost prohibitive locations and provides reliability with advanced accuracy for all installations.
Emerson's AMS Machinery Health Manager diagnoses and communicates the health of mechanical and rotating machinery using data from several predictive maintenance technologies. This includes integrating data from third-party, complimentary technology vendors such as mini labs and handheld oil analyzers from Spectro Scientific and popular infrared and thermography cameras from FLIR and Fluke.
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