Belt conveyor systems are widely utilized for continuous transport of bulk materials. Maintenance activities are essential to ensure the reliability of belt conveyor systems. Conventional diagnosis decision is achieved based on empirical constant thresholds. The Challenge of this study is to propose a framework of integrated maintenance decision making for belt conveyor idlers. Information from operational conditions, reliability estimation of idlers and condition monitoring data are integrated for accurate decision making. Innovatively, in the proposed framework threshold of the monitoring parameter can vary according to real time operational conditions and reliability estimation results. A simulation study is presented to demonstrate the effectiveness of framework. Simulation results show that the framework can result in more accurate maintenance decision making compared to conventional approaches.

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Belt conveyors play an important role in the dry bulk material handling process. Speed control is a promising method of reducing the power consumption of belt conveyors. However, inappropriate transient operations might cause risks like material spillage away from the belt conveyor. The unexpected risks limit the applicability of speed control. Current studies on speed control mainly focus on designing energy models of belt conveyors or building control algorithms of variable speed drives, while rare researchers take into account the risks in transient operations and the dynamic performance of belt conveyors under speed control. The paper proposes an Estimation-Calculation-Optimization (ECO) method to determine the minimum speed adjustment time to ensure healthy transient operations. The ECO method is composed of three steps and takes both risks in transient operations and the conveyor dynamics into account. In the Estimation step, an estimator is built to approximate the permitted maximum acceleration by treating the belt as a rigid body. Taking the belt's visco-elastic property into account, the Calculation step computes the conveyor dynamics by using a finite-element-method. With respect to the risks in transient operations, the Optimization step improves the conveyor's dynamic behaviors and optimizes the speed adjustment time. A case of a long belt conveyor system is studied and the ECO method is applied. The secant method is also used to improve the optimization efficiency. According to the experimental results, the ECO method is successfully used to determine the minimum speed adjustment time to ensure healthy transient operations, including both the accelerating and the decelerating operations. With the suggested adjustment time, unexpected risks are avoided and the belt conveyor shows an appropriate dynamic behavior. Accordingly, the ECO method ensures healthy transient operations and improves the applicability of speed control with the consideration of the potential risks and the conveyor dynamics.

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Belt conveyor systems are widely used in bulk material handling and transport applications. Within a belt conveyor, depending on its distance, there can be tens of thousands of idler rolls which face random failure. However, condition monitoring solutions for belt conveyor idlers is underdeveloped. This is because the choice of monitoring parameters is still arbitrary. This paper aims to investigate which parameters can represent technical condition of idler rolls for the purpose of condition monitoring. A belt conveyor test rig is developed in laboratory. Temperature and vibration sensors are applied to monitor idler rolls induced with different types of failures. Patten of temperature evolution and RMS level of vibration are extracted from the signal and analysed. It is concluded that temperature measurement at roll shafts is a straightforward and effective manner for condition monitoring of belt conveyor idlers.

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Large-scale belt conveyor systems are widely involved in medium and long distance transport of bulk material. The spatially distributed idler rolls are a great challenge to maintain the reliability of the conveyor systems. Automation of maintenance in intelligent ways provides a promising solution to replace traditional corrective maintenance. This paper further elaborates the concept of intelligent maintenance introduced by Lodewijks in 2004. We review state-of-the-art idler roll data acquisition, decision-making techniques and recent development of robotic roll replacement. A novel SD model based maintenance logistics control is proposed. The viability of robotic idler roll replacement has been demonstrated by case studies. Opportunities in the intelligent maintenance of idler rolls are discussed. Cloud-based intelligent maintenance is considered an emerging trend in the near future.@en