Abstract
Purpose.The aim of the study is to determine and analyse causes of faults in the operation of TR-7A scraper conveyor and to estimate the required time for their remediation and select the methods of their prevention and elimination. Methods. The characteristic of a system, such as the scraper conveyor, intended to fulfil its specified function in time and operation conditions, can be studied, theoretically, by determining its operational reliability. This implies the existence of a framework that incorporates several interconnected components of technical, operational, commercial and management nature. The quantitative expression of reliability was based on elements of mathematical probability theory and statistics (exponential distribution law), failure and repair mechanism not being subject to certain laws. Findings. The following TR-7A subassemblies, if defective, could have been the cause of a failure: chains, hydraulic couplings, chain lifters, drive, return drums, some electrical equipment. After 28 months of monitoring the TR-7A operation, we have established the number of failures (defects) ni, the operating time between failures ti, frequency of failures fc, time to repair tri, weight repair time pr, mean time between failures (MTBF), mean time to repair (MTR). Originality.Data collection and processing involves the adoption of specific procedures to allow the correct highlighting of the causes and frequency of failures. The accomplishment of this approach allowed finding the solutions for increasing reliability of some subassemblies of TR-7A conveyor (i.e., those subjected to abrasive wear). Practical implications.One solution was to use materials with compositional and functional gradient in the case of worn surfaces of some subassemblies. It was successfully applied for the chain lifters where a significant increase in the mean time between failures was obtained. The field of application of these materials can be extended to the metal subassemblies of machines and equipment with abrasion wear that occurs both in underground mines and in quarries.
Highlights
High processing computing has led to an extensiveAll over the world, researchers and engineers who are concerned about improving underground or surface mining processes constantly deal with various tasks aimed at enhancing reliability of technical equipment to optimize its operation, and increase productivity
Researchers and engineers who are concerned about improving underground or surface mining processes constantly deal with various tasks aimed at enhancing reliability of technical equipment to optimize its operation, and increase productivity
Recent trends in this regard have led to the development of Adaptive Deep Convolutional Network methods which can better meet the real-time and reliability requirements of conveyor belt damage detection [1]
Summary
High processing computing has led to an extensiveAll over the world, researchers and engineers who are concerned about improving underground or surface mining processes constantly deal with various tasks aimed at enhancing reliability of technical equipment to optimize its operation, and increase productivity. Conveyers are considered a critical component of modern coal mining transportation system; it is essential to diagnose and monitor their damage accurately. Recent trends in this regard have led to the development of Adaptive Deep Convolutional Network methods which can better meet the real-time and reliability requirements of conveyor belt damage detection [1]. The conveyor, the most important subsystem in a mining scraper, is a highly coupled, chain drive system which has been analysed based on dynamic meshing properties. Contact analysis based on meshing properties is useful in describing the dynamic properties of the chain drive system in detail [4]
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