An Unsupervised Method for Characterising the Operation of Bulk Handling Conveyor Belt Systems In-Service

  • Owen C Freeman Gebler

Student thesis: Doctoral ThesisEngineering Doctorate (EngD)

Abstract

Conveyor belt systems (CBS) are a fundamental class of asset throughout bulk handling industries, which provide materials transfer functionality within processes spanning all stages of a material’s life, from initial extraction through to end-of-life disposal or reuse. The generic function a CBS affords can result in a single system being used in a diverse range of applications throughout its service life, including exposure to different materials, environments and throughputs. However, regardless of the specific application a CBS will be expected to realise very high levels of availability, such that the occurrence of costly process downtime is minimised.
To support the realisation of such levels of availability, increasingly across industry continuous monitoring (CM) of systems is being implemented, to provide greater insight into the operation of systems. Development within the area of CM has historically been driven by the needs of high value industries such as aerospace and renewables, thus, it cannot be assumed that existing solutions represent the most appropriate form of CM for CBS applications. Furthermore, the interrogation of raw CM parameters requires effort and expertise from practitioners in order to elicit actionable insights, which cannot be assumed available across operations.
Accordingly, this thesis reports the development of a practical method for observing the operation of a CBS in-service, using continuous monitoring techniques. Initially, current practice and challenges faced by industrial practitioners are investigated through interactions with a manufacturer and an operator of bulk handling CBSs. From these interactions an opportunity to realise improvements across the design, operation and maintenance of CBSs through the adoption of CM is established. Next, through a series of laboratory and industrial trials a range of CBS parameters are investigated and evaluated within the context of continuous monitoring, from which motor electrical power consumption (MEPC) is identified as most appropriate given the specific characteristics of bulk handling applications. Finally, a data processing method for translating raw MEPC parameters into actionable insights is developed, comprising the production of three data descriptors, each of which provides a quantification of a specific aspect of CBS operation, related to its usage or health. Finally, the application and potential utility therein of the method is demonstrated through further industrial trials.
Date of Award29 Sep 2020
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorJason M Yon (Supervisor), Dawei Han (Supervisor) & Ben J Hicks (Supervisor)

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