The ultimate objective of this study was to identify and/or propose graph reduction techniques that can be used to reduce the complexity of the attributed graph data of the Tennessee Eastman process. These techniques had to reduce graph complexity while ensuring that the FDI methods applied to the TEP graph data retain the level of performance achieved before any reduction had taken place, thereby proving that graph reduction as a concept is a viable solution.
Before any form of graph reduction could take place, control data of the performance of the FDI methods when the standard, unreduced attributed graph data are used to diagnose faults, had to be generated. The control data are used to evaluate the efficacy of the reduction techniques.
The distance parameter, eigendecomposition, and residual-based FDI methods are the three FDI methods used to diagnose faults from the TEP graph data. The eigendecomposition FDI method was updated according to the findings of a concurrent study to reduce the complexity of the FDI method by only considering dominant eigenvalues.
The indicators used to quantify the performance of each FDI method are the overall detection rate, overall isolation rate, and the specific isolation rate of critical fault conditions. These in- dicators were selected to facilitate a holistic evaluation while not overwhelming the evaluation process with excessive amounts of data. When looking at these indicators for the three FDI methods, it is clear that there is great variety in the performance indicators across the differ- ent methods, making it challenging to select a top-performing method for the TEP. However, this variety in performance indicators across the different methods in the control data set will contribute to a robust evaluation of graph reduction techniques.
The graph reduction techniques proposed for this study are based on three theoretical con- cepts. The first concept is that attributes experience slight variation from the NOC for all fault conditions, have a negligible impact on the FDI process and can be removed. The sec- ond concept is that attributes with small values contribute less to the FDI process and can be removed. The final concept is that graph complexity decreases by methodically summa- rizing nodes with similar attribute values with imposed limitations. This preserves attribute information.
From these three concepts, five graph reduction techniques were formulated. The first two concepts have a technique based on node attributes and a technique based on link attributes.
The final concept has one technique based on node attributes only. The five techniques were formulated to remove structural information from the graph data without distorting the remaining structural information and resulting in additional processing resources being required. Furthermore, structural information is removed methodically, making it easier to reconstruct the original graph. This was done to overcome the shortfalls of the techniques identified in the literature.
An experimental process had to be used to determine if the proposed reduction techniques formulated from the three theoretical concepts could reduce graph complexity without de- teriorating FDI performance. This experimental process involves applying each reduction technique iteratively and increasing the extent to which the technique reduces the TEP graph data with each iteration. After each iteration, the FDI performance indicators are recorded.
This is done until there are no more attributes to reduce or the limitations and/or restrictions prevent the technique from reducing any further. Then, the performance indicators obtained after each reduction iteration were compared to the control data set indicators by plotting some of these indicators as more attributes were reduced. This was done to determine the efficacy of each technique.
Several interesting observations could be made from the performance of each FDI method’s response to the reduction iterations of each reduction technique. However, the most important
observation that can be made is the fact that for every FDI method applied to the TEP graph data, it was possible to reduce the graph data with at least one reduction technique using a specific reduction iteration while maintaining a similar level of performance as achieved in the control data set.
Another important observation that could be made is the occurrence of anomalies in the form of local minima or maxima as the reduction interval increases. These anomalies indicate that there exists structural information in the attributed graph data that can be either vital or non-vital to FDI performance. This observation inferred that the downward part of either the local minima or maxima results from vital information that bolsters FDI performance, being removed from the graph data. Conversely, the upward part of either the local minima or maxima results from non-vital information that obfuscates FDI performance, being removed from the graph data.
To determine which reduction techniques are more effective than others, the complementary techniques of every FDI method were identified. Each complementary technique effectively reduces graph data while maintaining a similar level of FDI performance achieved in the control data set. For the TEP, the reduction techniques based on attribute variation (Techniques 1
& 2) were much more effective than those based on attribute size analysis (Techniques 3 &
4). Across all three FDI methods, Technique 5 resulted in the most stable response of FDI performance for its reduction range.
To bypass the shortcomings of some of the individual reduction techniques, it was proposed that specific individual techniques be combined by applying them to the graph data simulta- neously. Thus, for each FDI method, that method’s complementary reduction techniques at specific reduction intervals were applied simultaneously, and the standard performance indi- cators and the execution time of the FDI method were recorded. By doing this, it was found that for the TEP graph data, the performance and/or execution time of each FDI method improved relative to the performance and/or execution time achieved when the individual complementary reduction techniques were applied. The effect some of the individual and combined reduction techniques had on the execution time of FDI methods, can be observed in Sections 6.3 & 6.4. In some instances, the combined techniques resulted in a superior level of FDI performance than that achieved in the control data.
For the verification of this study, it had to be verified that all five reduction techniques were implemented as proposed. Therefore, each technique was first implemented in the MATLAB® to reduce an attributed graph to a certain extent. The technique was then implemented in Excel® to reduce the same attributed graph to the same extent. Finally, these two attributed graphs were compared to each other in their NSM format. Since both matrices were identical
for each technique, it can be concluded that the study correctly implemented and verified each proposed reduction technique.
The validation of this study entails showing that the proposed solution, in this case, the concept of graph reduction, is a general solution. This involves showing that at least one of the reduction techniques proposed in this study can reduce the attributed graph data of more than one validated process model while maintaining a similar level of FDI performance achieved prior to the graph data of those process models being reduced. To this end, a validated GTLP model was used as an additional model to evaluate the graph reduction techniques. The GTLP model differs from the TEP model with regards to the type of fault conditions, complexity and structure of the attributed graph, and the composition of graph data (steady-state model vs dynamic model). These differences help to enhance the generality of the solution.
To evaluate the proposed reduction techniques on the GTLP, the same chain of events used with the TEP was repeated. First, the control data set was generated by applying the FDI methods to the standard, unreduced attributed graph data of the GTLP. Then, each reduction technique’s response on each FDI method’s performance was evaluated using an experimental process that applies the techniques iteratively, increasing the reduction interval with each iteration. Thus, the performance of each FDI method was determined at every reduction increment, and the indicators of each FDI method at these various reduction intervals could be compared to the indicators in the control data set.
This evaluation yielded that it is possible to reduce the attributed graph data of the GTLP while maintaining the FDI performance achieved prior to any graph reduction taking place.
Furthermore, since graph reduction could successfully be applied to both the TEP and the GTLP, which are both validated models, it can be concluded that graph reduction is a general solution, which ultimately validates this study. It should be stressed that this study does not aim to compare the performance of FDI methods using TEP data with the performance obtained using GTLP data. Instead, the aim is to investigate and compare the response graph reduction has on the TEP FDI performance with the response it has on GTLP FDI performance to determine if it is a viable and general solution.
When comparing the response on FDI performance after applying the reduction techniques to the GTLP graph data to the response after applying the techniques to the TEP graph data, anomalies in the form of local minima and maxima were again detected. This supports the argument that certain reduction operations remove vital structural information (causing the downward part of either the local minima or maxima). In contrast, certain other reduction operations remove non-vital structural information (causing the upward part of either the
local minima or maxima). This results in FDI performance being obfuscated or bolstered, respectively.
When comparing the efficacy of the five different techniques applied to the GTLP graph data, it can be noted that the techniques based on attribute size analysis (Techniques 3 & 4) were more effective when applied to the GTLP data than they were applied to the TEP data.
However, seeing as the variance-based reduction techniques (Techniques 1 & 2) were effective when applied to the data of both models, it can be inferred that they are more effective in general than the techniques based on attribute size analysis.
Combining reduction techniques and applying them to the GTLP graph data did not yield the same successful response as achieved when techniques were combined and applied to the TEP graph data. While it could be shown that combining specific complementary techniques for each FDI method had the possibility of being more effective than some of the individual complementary techniques, an individual complementary technique at a specific reduction interval was identified for each FDI method that could not be outdone.