by the total number of cases can be misleading. This is the case if the fault cases classified incorrectly are those from the less numerous cases. To remedy this situation a measure of accuracy called geometrical accuracy (GA) is used and defined as: 

$$\mathrm{G}\mathrm{A}=$$
$\left(19\right)^2$
1 n 1 n q **...q**
c **...c**
GA∏
∏
= **(19)** 
where ∏ is the product, cn is the number of nth **fault cases classified correctly while q**n is the nth **fault class. Using this measure the MLP gives the accuracy of 0.7.On training** 
the SVM, there are different parameters that can be changed namely the capacity and the e-insensitivity, the amount of training inputs and the function to be used for the kernel. Some of the functions that can be used are: linear, radial basis function, sigmoid, and spline. In this paper the exponential radial basis function is used as a kernel. The training process took 45 CPU seconds and the capacity was set to infinity. The results obtained are shown in Table 3 and these results show that the SVM gives accuracy of 94% while it gives the GA of 0.92. GMM architecture on the other hand used diagonal covariance matrix with 3 centers. The main advantage of using the diagonal covariance matrix is that this de-correlates the feature vectors. The training process took 45 CPU seconds. Table 4 shows that the GMM gives accuracy of 98% while it gives the GA of 0.95. As can be seen from Tables 2, 3 and 4, the GMM outperforms, the SVM network which out-performed the MLP. The MLP network. Table 2. **The confusion matrix obtained when the MLP network is used for fault classification** 

|        | Predicted   |       |       |       |       |       |       |       |    |
|--------|-------------|-------|-------|-------|-------|-------|-------|-------|----|
|        | [000]       | [100] | [010] | [001] | [110] | [101] | [011] | [111] |    |
|        | [000]       | 39    | 0     | 0     | 0     | 0     | 0     | 0     | 0  |
|        | [100]       | 0     | 3     | 0     | 0     | 0     | 0     | 0     | 0  |
|        | [010]       | 0     | 0     | 3     | 0     | 0     | 0     | 0     | 0  |
| Actual | [001]       | 0     | 0     | 0     | 3     | 0     | 0     | 0     | 6  |
|        | [110]       | 0     | 0     | 0     | 0     | 3     | 1     | 0     | 0  |
|        | [101]       | 0     | 0     | 0     | 0     | 0     | 2     | 0     | 0  |
|        | [011]       | 0     | 0     | 0     | 0     | 0     | 0     | 3     | 4  |
|        | [111]       | 0     | 0     | 0     | 0     | 0     | 0     | 0     | 29 |

Table 3. **The confusion matrix obtained when the SVM network is used for fault classification**

|        | [000]   | [100]   | [010]   | [001]   | [110]   | [101]   | [011]   | [111]   |    |
|--------|---------|---------|---------|---------|---------|---------|---------|---------|----|
|        | [000]   | 39      | 0       | 0       | 0       | 0       | 0       | 0       | 0  |
|        | [100]   | 0       | 3       | 0       | 0       | 0       | 0       | 0       | 0  |
|        | [010]   | 0       | 0       | 3       | 0       | 0       | 0       | 0       | 0  |
| Actual | [001]   | 0       | 0       | 0       | 3       | 0       | 0       | 0       | 5  |
|        | [110]   | 0       | 0       | 0       | 0       | 3       | 0       | 0       | 0  |
|        | [101]   | 0       | 0       | 0       | 0       | 0       | 3       | 0       | 0  |
|        | [011]   | 0       | 0       | 0       | 0       | 0       | 0       | 3       | 1  |
|        | [111]   | 0       | 0       | 0       | 0       | 0       | 0       | 0       | 33 |
