Thesis (MSc)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Rock falls are the cause of the majority of mining-related injuries and fatalities in
deep tabular South African mines. The standard process of entry examination is
performed before working shifts and after blasting to detect structurally loose rocks.
This process is performed by a miner using a pinch bar to ‘sound’ a rock by striking
it and making a judgement based on the frequency response of the resultant sound.
The Electronic Sounding Device (ESD) developed by the CSIR aims to assist in this
process by performing a concurrent prediction of the structural state of the rock
based on the acoustic waveform generated in the sounding process. This project
aimed to identify, develop and deploy an effective classification model to be used on
the ESD to perform this assessment.
The project was undertaken in three main stages: the collection of labelled acoustic
samples from working areas; the extraction of descriptive features from the waveforms;
and the competitive evaluation of suitable classification models.
Acoustic samples of the sounding process were recorded at the Driefontein mine
operation by teams of Gold Fields employees. The samples were recorded in working
areas on each of the four reefs that were covered by the shafts of the mine complex.
Samples were labelled as ‘safe’ or ‘unsafe’ to indicate an expert’s judgement of the
rock’s structural state. A laboratory-controlled environment was also created to
provide a platform from which to collect acoustic samples with objective labelling.
Three sets of features were extracted from the acoustic waveforms to form a
descriptive feature dataset: four statistical moments of the frequency distribution of
the waveform formed; the average energy contained in 16 discrete frequency bands
in the data; and 12 Mel Frequency Cepstral Coefficients (MFCCs).
Classification models from four model families were competitively evaluated for
best accuracy in predicting structural states. The models evaluated were k-nearest
neighbours, self-organising maps, decision trees, random forests, logistic regression,
neural networks, and support vector machines with radial basis function and polynomial
kernels. The sensitivity of the models, i.e. their ability to avoid predicting a
‘safe’ status when the rock mass was actually loose, was used as the critical performance
measure.
A single-hidden-layer feed-forward neural network with 15 nodes in the hidden
layer and a sigmoid activation function was found to best suited for acoustic classification
on the ESD. Additional feature selection was performed to identify the
optimised form of the model. The final model was successfully implemented on the
ESD platform. / AFRIKAANSE OPSOMMING: Rotsstortings is die oorsaak van die meerderheid van mynbouverwante ongelukke
en ongevalle in diep tabulêre Suid-Afrikaanse myne. Die standaard proses van pretoegang
ondersoeke om strukturele los rotse te erken, word uitgevoer voor enige
werkskof en na skietwerk. Dit word gedoen deur ‘n myner wat ‘n breekyster teen
die rots kap en ‘n oordeel vel op die frekwensie weergawe van die gevolglike klank.
Die ‘Elektroniese Klinking Toestel’ (Electronic Sounding Device, ESD) is ontwikkel
deur die WNNR met die doel om die proses te ondersteun. Dit word gedoen deur
‘n gelyktydige voorspelling van die strukturele toestand gebaseer op die akoestiese
golfvorm gegenereer in die proses van klinking. Die projek stel ten doel om ’n effektiewe
klassifikasie-model te identifiseer, te ontwikkel en toe te pas in die ESD om
hierdie assessering uit te voer.
Die projek vind in drie stadiums plaas: die insameling van geëtiketteerde akoestiese
monsters van die werkareas; die ekstraksie van beskrywende kenmerke van die golfvorms
en die mededingende evaluering van geskikte klassifiseringsmodelle.
Klinking akoestiese monsters is opgeneem by Driefontein mynbouoperasie deur
spanne van Gold Fields se werknemers. Die akoestiese monsters is opgeneem in
werkareas van elk van die vier goudriwwe wat deur die skagte van die mynkompleks
gedek word. Monsters is as ‘veilig’ of ‘onveilig’ geëtiketteer as aanduiding
van die ekspert se oordeel van die rots se strukturele toestand. ‘n Laboratorium
gekontroleerde omgewing is ook geskep om ’n platform te skep vanwaar akoestiese
monsters met objektiewe etikettering waargeneem word.
Drie stelle van kenmerke is onttrek van die akoestiese golfvorms om ‘n beskrywende
datastel van kenmerke te vorm: vier statistiese momente van die frekwensie
verspreiding van die gevormde golfvorm; gemiddelde energie ingesluit in sestien
diskrete frekwensiebande in die data; en twaalf ‘Mel Frequency Cepstrum Coefficients’
(MFCCs).
Klassifikasie modelle van die vier modelsamestellings was kompeterend geëvalueer
vir die beste akkuraatheid in voorspellings van strukturele toestande. Klassifikasie
modelle het k-naaste bure, selforganiserende kaarte, besluitnemingsbome, lukrake
woude, logistieke regressie, neurale netwerke en steun-vektor masjiene met radiale
basisfunksie en polinominale kerne. Die meting van die sensitiwiteit van die modelle,
met betrekking tot die vermoë van die modelle om veilige voorspellings te beperk
wanneer die rotsmassa los is, was gebruik as ’n kritiese werksverrigtingsmeting.
‘n Enkel-verskuilde-laag neurale netwerk met 15 nodes in die verskuilde laag en ’n
sigmoïde aktiveringsfunksie is gevind as die mees geskikte vir die ESD. Addisionele
keuse van kenmerke is uitgevoer deur die geoptimiseerde vorm van die model te
identifiseer. Die model was suksesvol geïmplementeer op die ESD platform.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/96985 |
| Date | 03 1900 |
| Creators | Brink, Stefan |
| Contributors | Aldrich, Chris, Dorfling, Christie, Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | xx, 139 pages : illustrations |
| Rights | Stellenbosch University |
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