Economic lifetime prediction of a mining drilling machine using an artificial neural network

Figures & data

Figure 1. Drilling machine.

Figure 2. Optimal structure of ANN model.

Figure 3. Producing 400 input data for ANN as part of case three.

Figure 4. Learning capability: Output of the proposed ANN for seen data.

Figure 5. Generalisation capability: Output of the proposed ANN for unseen data.

Table 1. Weights and threshold levels for the proposed ANN.

Hidden layer nodes	wii weights from node ith input layer to node jth hidden layer			Hidden threshold (θj)
	i = 1	i = 2	i = 3
j=4	−0.0648	−0.03030	−0.1297	10.4780
j=5	−2.6814	−0.78166	−2.1337	0.18142
j=6	−1.2496	−0.8258	−2.3112	5.023854
Output layer nodes	wij weights from node ith hidden layer to node jth output layer			Output threshold (θj)
	i = 4	i = 5	i = 6
j=7	7.795	−5.3591	−10.801	2.919081

Figure 6. Relative importance of the variables affecting the ORT of the drilling machine.

Figure 7. Effect of IPP and DMC for 25% OC decrease.

Figure 8. Effect of DOC and DMC for 25% PP increase.