Artificial Neural Networks

It is commonly accepted that the most powerful processor in existence is the human brain. Even if we omit or degrade the ability of the brain to process data very rapidly, in a way that no modern processor can, the human brain has the unique and very important ability to perform certain functions effectively by using examples from stored knowledge. This could be defined as experience, and it is the most desirable function that a diagnostic tool could perform.

As such, there have been considerable efforts on the part of scientists to create systems that emulate the human brain by means of data processing, storage and training. Such systems are generally termed as Artificial Intelligence Systems. Concerning applications related to turbine engine diagnostics, maybe the most representative example of such is Artificial Neural Networks, which appear to be very effective, with promising future applications. Artificial Neural Networks are classified as performance diagnostic methods, but they have one significant difference from other methods, which also constitutes their advantage, namely, the ability of such a system to effectively train itself, without the need for any pre- defined rules or models (Li, 2002).

In order to describe in a simple way how an Artificial Neural Network diagnostic system works, it is necessary to identify the most important elements of such a system. At the centre of this kind of diagnostic system is the neuron; in other words, the data processing unit, each of which is assigned to a different function. Each has the ability to process the data in parallel and produce an output of a weighted sum. The different weights come from the different algorithms that every neuron must have in order to perform its function. For diagnostic systems that use Artificial Neural Networks as the diagnostic method, numerous neurons are needed.

Because of the requirement for many neurons, the system is divided into layers, each of which has its own dedicated neurons. Connections, or synapses, between the neurons enable the flow and processing of information, depending on the role of the particular neuron. These synapses have their own weights, which are derived from the different algorithms pertaining to each connection. Essentially these weights provide the neurons with the most appropriate data for processing and storing information (Byington et al., 2002). Additionally, and very importantly, depending on those connections there is a distinction between the types of network. This distinction concerns the type of the data flow, which

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could either be a forward flow to every successive layer, or both forward and backward flow, to enable the system to be trained via the feedback process (Jain et al., 1996).

The presence of these layers is essential and a general description, which is the purpose of this thesis, is sufficient. Put simply, there is an input layer, an output layer, and between them are one or more hidden layers. As mentioned above, several neurons are located within every layer. The need for fault detection, isolation and quantification creates the structure of these layers, and particularly the input and the output layer. Hence a complete network is constructed from several sub-networks which are dedicated to specific components, and also to specific failure modes of every component.

In order for a system to be operational it must pass through two general phases. The first phase is the training mode. In this stage the network is fed not only with the kind of the data that it will process during its life, but also with the sort of failures that might occur. Generally the sets of data required for the training phase are the training data, the target outputs and the test data. After that phase is completed, the system is in the recall mode, in which it is able to perform the diagnostic/prognostic requirements. These two general phases are part of every kind of network (Joly et al., 2004).

Even if there are distinctions that separate the artificial networks into many categories, it is the authors’ opinion that the basic distinction concerns the function of the network. As was previously noted, the three requirements of fault detection, isolation and quantification provide the shape of the basic structure of the network; hence there are Artificial Neural Networks for each of those functions, with the difference lying in the input and output layers, or more specifically, the neurons that are within those layers (Patel et al., 1996b).

Beginning with fault detection the network architecture has a simple form, consisting of three layers, with the last output layer having only one neuron. In general the purpose of such a network is simply to indicate whether there is degradation, which might indicate a fault for the engine. The input layer has the same number of neurons as the monitored components or parameters. Conversely, the output layer has only one neuron, which shows whether or not the engine has a fault, with discrete signals of zero (0) and one (1) for example (Figure 2.10). However for all kinds of networks the input layer should have an equal number of neurons as the required monitored parameters (Lu et al., 2001; Simani and Fantuzzi, 2000).

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The same three layer architecture is applied for the fault isolation, the only difference being that here the output layer has an equal number of neurons as the components or parameters under investigation, and separate results are needed for each of them. The discrete signal of 0 and 1 can also apply here, but in this context it indicates the specific components which are faulty, and not the sum of faults for the whole engine. The difference between this network and the fault quantification network is that the output signals will be more complex from a mathematical point of view than the discrete signals of 0 and 1 (Li, 2008).

Figure 2-10: Fault Detection network (Li, 2008)

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The rest of the separations concern factors like the kind of training mode, the type of data flow etc. As far as the Artificial Neural Networks that are used for gas turbine diagnostics are concerned, the most common types in use are:

 Feedforward back propagation neural networks

 Probabilistic neural networks

 Self-organising maps

 Learning vector quantization networks

 Counter propagation networks

 Adaptive resonance theory networks

 Resource allocating networks

 Recurrent cascade correlation neural networks

The first category of feedforward back propagation neural networks has been found in many publications to be the best solution. It should be mentioned that the analysis of each of the above is beyond the scope of this research, and that further information about each type can be found in the references given in Li (2002).

In summary, whatever the kind of network, Artificial Neural Networks have the advantage of being able to perform parallel processing of data and operating satisfactorily, even with limited information. In addition, they are very tolerant of noise and bias problems, and most importantly for a turbine engine, they can handle the non-linear relationships of dependent and independent parameters. On the other hand, the disadvantages are also important. The main disadvantage is that the optimal network architecture/structure for a given problem is not known from the optimum data sets for monitoring. Finally, the difficulty of defining the optimum training criteria is something that limits the system (Joly et al., 2004; Ogaji and Singh, 2003; Patel et al., 1996a). A typical diagnostic system based on artificial neural networks is shown in the following Figure 2.13.

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