Insights

CoCoP presentation

Cocop

Kammal Al-Kahwati, Analytics and software specialist at Predge held a presentation at the 5th International Conference on Control and Fault-Tolerant Systems – SysTol’21 (29th of September – 1st of October).

Read more about our solution Predge Conveyor™

Abstract: Predictive maintenance strategies for the mining sector are of utmost importance considering the automated behavior of industrial systems and the oftentimes inaccessible environment around belt conveyor systems. In this paper, we present a model combining IoT sensors and dead-reckoning modeling, focused on early theoretical work in the field of modeling the behavior of belt conveyor systems to act as a decision support tool in maintenance strategies, by estimating the remaining useful life (RUL) of rotating components in a belt conveyor system. The estimation of RUL is a function of the degradation of the ball bearings in idler rollers due to the forces acting on the rollers during the conveyance of material. The forces occur due to the material loading, the belt weight, roller shell weight, and the idler misalignment load (IML). Furthermore, the dynamics of bulk material during conveyance can be modeled in several ways considering earth pressure theory. A model considering this is derived from the Krausse Hettler method to determine the forces acting on the wing rollers of a thee-roll idler trough set by the notion that the bulk material undergoes active and passive stress states during conveyance. The model is further compared and extended to the works of Sokolovski, to get a bounded delta RUL reduction estimate on the roller bearings in each idler set of a belt conveyor system.

CoCoP – Conveyor Belt Condition Monitoring and Health Prediction

The CoCoP project develops and validates a break-through innovative and flexible SaaS solution for condition monitoring and health prediction of belt conveyor systems. Contrary to available solutions on the market, a system level approach is taken to understand wear and tear of critical components and to predict when the systems will fail. Combining data from all around the belt conveyor system with the physical behavior and operational characteristics, component degradation will be predicted and timely actions for operation and maintenance will be prescribed.