Fault identification of preparation tank blockage using linear cross-correlation

Since it was observed in section 6.6.1 that LC gave the best identification results, these results are presented in this section.

Fault identification using linear cross-correlation with unblocked contributions

Contributions of each variable to the PCA SPE in the unblocked case are plotted in Figure 6-14. Variables whose contributions rose above the contribution from the validation data, i.e. those that rose above the dashed red line are taken to be symptom nodes.

The unblocked contribution plot shown in Figure 6-14 indicates that MFR1, MFR5, MFR13 and MTK10 displayed large contributions. This is consistent with the blockage fault, since the blockage would restrict MFR5, causing MTK10 to deviate from its set-point. Subsequently the level controller would vary MFR1 to correct for this deviation.

Chapter 6 -Case Study: Fault Diagnosis in Second and Third Stage Leaching Simulation Page 132

Figure 6-14: Contribution plot for PCA SPE for unblocked data for preparation tank blockage fault

Chapter 6 -Case Study: Fault Diagnosis in Second and Third Stage Leaching Simulation Page 133 Applying back propagation in the unblocked LC graph from the symptoms identified by contributions plot gives MFR15, T9 and MFR 14 as possible root nodes, as shown in Figure 6-15. It seems that the symptom nodes gave a better indication of the fault conditions. However, as noted earlier, this system’s flow rate and temperatures are very sensitive to changes in the flash recycle tank, which cause T9 and MFR9 to fluctuate. Therefore the identified root nodes may be indicative of the fault conditions.

Figure 6-15: Back propagation in the unblocked linear cross-correlation connectivity graph using the symptoms identified from contributions (shown in blue). Possible identified root nodes and propagation

paths are shown in red

Fault identification with linear cross-correlation using blocked contributions

The contribution plot for the LC block that showed the best detection results is given in Figure 6-16. This resulted in MFR7 and MFR17 showing increased contributions. Since MFR7 is directly downstream of the blockage, this gives a good indication of the fault conditions. The contribution of MFR17 also indicates that the blockage has a profound effect on downstream flow rates, which is

Chapter 6 -Case Study: Fault Diagnosis in Second and Third Stage Leaching Simulation Page 134 most likely due to the numerous control loops in the process; a blockage right at the start of the process causes all the flow rates to deviate from their set-points, causing each controller to take aggressive control action which causes the fault to propagate downstream.

Figure 6-16: Contribution plot for Block 3 from linearcross-correlation

Applying back propagation in the blocked LC graph from the symptoms identified by contributions in this block results in MFR7 being identified as a possible root node, as shown in Figure 6-17. Since this is directly downstream of the blockage, this is a very accurate indication of the fault conditions

Figure 6-17: Back propagation in linear cross-correlation graph for block 3 using the symptoms identified from contributions. Possible identified root nodes and propagation paths are shown in red

Fault identification with linear cross-correlation using unblocked connectivity changed

Considering the connectivity change in the unblocked LC graph (shown in Figure 6-18), many symptom nodes were identified, including MFR1, MFR5 and MTK10, which gives a good indication that a blockage has occurred.

Chapter 6 -Case Study: Fault Diagnosis in Second and Third Stage Leaching Simulation Page 135

Figure 6-18: Fault conditions linear cross-correlation connectivity for preparation tank blockage fault, showing symptom nodes (highlighted in blue) identified from connectivity change

Applying back propagation in the unblocked connectivity graph from these symptoms did not result any root nodes being identified, as shown in Figure 6-19. Therefore no further fault identification information was revealed. This unsuccessful back propagation is due to the large amount of symptom nodes; no common ancestors could be identified.

Chapter 6 -Case Study: Fault Diagnosis in Second and Third Stage Leaching Simulation Page 136

Figure 6-19: Back propagation in the unblocked linear cross-correlation graph using the symptoms (highlighted in blue) identified by connectivity change

Fault identification with linear cross-correlation using blocked connectivity change

Since KCPA and PCA both showed the best results for block 3 the connectivity change results are the same for both. Considering the change in LC from NOC to fault conditions (shown in Figure 6-20) in the block that gave the best results for PCA and KPCA, T7 were identified as symptom nodes and MFR7. This gives a very good indication that a blockage upstream of the recycle tank occurred, since it would strongly affect MFR7 and subsequently the temperatures in the tank.

Chapter 6 -Case Study: Fault Diagnosis in Second and Third Stage Leaching Simulation Page 137 MFR7, MTK20 and MFR9 were identified as possible roots using back propagation in the LC graph for block 3, as shown in Figure 6-21. This gives a very good indication of the fault conditions since this affects the tank directly downstream form the blockage.

Figure 6-21: Back propagation applied to the linear cross-correlation graph for block 3 using symptoms identified from connectivity change (in blue). Possible root nodes and propagation paths are shown in red