The existence of humankind on Earth largely depends on the energy at its disposal. It is mostly generated by processing minerals extracted from the Earth’s crust by open-cut mining. The quality and low cost of extraction are largely defined by the dependability of employed machines and mechanisms, plants and process engineering solutions. Various types of excavators are the backbone of a mining machine fleet. Their parts that principally interact with the environment (rock) are components of implements, i.e. primarily the buckets and components of bucket(s). It must be noted that in the process of interaction with the environment (rock) the excavator implements and their components are exposed to so-called abrasive wear. Since abrasive wear of implement components (most frequently excavator bucket teeth) causes their recurrent replacement, this inevitably affects the performance of the excavator as a whole and those process flows it is part of. Occasional interruptions of operation and repairs reduce the availability factor, the most important complex indicator of equipment dependability. Given the above, the aim of this paper is to refine the previously known formula proposed more than thirty years ago in VNIISDM (Reysh A.K.) for evaluation of the rate of abrasive wear of excavator bucket teeth. For the first time, with a sufficient accuracy we examined the multitude of operating modes of mining equipment, i.e. operation of excavators in various conditions, e.g. on different soils. Additionally, we extended Reysh’s approach from single-bucket machines to continuous operation multi-bucket ones. For that purpose, the authors used a method of data integration from known sources, method of full-scale experiment under the operating conditions of a specific excavator and method of mathematical simulation (a form of the Monte Carlo method). All of that allowed revising the values of the parameters in the Reysh formula. The refined formula that we obtained can now be used for the dependability evaluation of machines operating under varying conditions, as well as for the purpose of appointing the time of preventive inspections.
Structural Health Monitoring (SHM) has progressed rapidly over the past five years because of new developments in digital processing and wireless communications. Previous concerns were that SHM systems supplied large amounts of data, which require time-consuming analysis. Because of new advancements in digital processing, data is analyzed on the structure in real-time so answers can be sent directly to the client or automatically shut down processing units. New SHM systems are autonomous to the point of self-diagnosis, such as determining if there is a severed cable or failed sensor. This presentation will discuss system advances as well present in-service monitoring case studies including, nuclear reactor, valves, wireless ultrasonic thickness, wind turbines, pipeline leak detection, offshore oil platform, turbine stator blade cracking, and boiler tube leak detection monitoring. The largest SHM example to date is the San Francisco Oakland Bay Bridge. This will demonstrate the applicability for other industrial applications where fracture critical eyebars were monitored for fatigue cracks. This unique application used 640 sensors to monitor the entire length of 384 eyebars or a total length of 3.94 miles. In this case study, an alarm example will show how metal fatigue-wear could have resulted in additional damage to fracture critical eyebars and a potential repair cost of over $14-million.
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