Statistics for Mining Engineering

Book Preface

The variety of technical devices that is available on the market is quite rich. The items offeredhave, in the majority of cases, the broad possibility of realizing their targets. Very often the devices that are selected are set up together, creating systems to execute predetermined functions. In mining engineering, mechanized systems are formed in order to excavate rocks, to haul broken material, to dump waste, to store excavated ore or to carry out ore treatment by mechanical, chemical or thermal means. The technical objects being used nowadays are complicated constructions. In order to apply them in an appropriate manner, we should follow the recommendations formulated by their producers and we should frequently observe the realization of their operation (exploitation)1 process, which is understood here as the process of changes in their properties during the use of the object. Running changes are interesting first of all for the constructor and producer of the object. They are also interesting for the user of the equipment, who is concerned in its application to attain the best performance of the item purchased over the long term. Information gathered during the operation of the object creates statistical data.

Actually, the more complicated machines used in mining (and pieces of equipment from other engineering areas) are furnished with special systems which diagnose a continuously running machine, collect information on its exploitation events and send it to the main producer’s computer. In many cases the information that is transmitted is very rich comprising a few tens of units of information. Again, this trio: the constructor, producer and user are interested in the accumulated material. And once again, this information has statistical character.

The information that is generated through the realization of the operation of a machine has a twofold meaning. It provides information on the technical parameters that are of interest at a given moment of time directly, e.g. power consumption, force applied during retardation, mass accumulated in the shovel etc. But very often there is concern about the average characteristics of the observed data, i.e. there is concern about the statistical characteristics of collected material.

It is easy to enumerate at least several further areas of mining engineering interest in which the important information that is gathered has a statistical character; investigations made during research on a stand in a lab generating records, collected data from an instrument with a new device installed and operating in real working conditions, sensors continuously tracing excessive shocks in a rock mass near mining production areas, information taken from an ore processing plant and connected with a given stage of ore treatment etc. All of these examples show areas that generate information of a statistical character. Information that should be analysed carefully; its synthesis should be conducted and statistical inference should be carried out.

The main goal of this book is to acquaint readers with a basic knowledge of mathematical statistics; knowledge that should be known by contemporary mining engineers. For this reason this book is addressed—first of all—to students of mining faculties and schools. The majority of the cases considered here are taken from the field of mine mechanization. However, there are also practical examples taken from different areas such as ore treatment, blasting, rock mechanics and some econometric problems associated with mining engineering.

Some parts of this book that deal with the reliability and exploitation problems of a variety of equipment will be interesting for reliability engineers. Similarly, some examples may be of interest for earthmoving engineers. Because of the universal character of the models and methods presented here, some parts may also be attractive for civil engineers, engineers involved in production as well as for engineers working at industrial institutes, especially mining ones. Students learning mathematical statistics and searching for practical applications of it will find somec hapters interesting as well. Additionally, I hope that my academic colleagues working at mining universities will also find this book interesting, and perhaps useful in their educational work.