*1.2. The Novelty of Current Research*

The fundamental goal of this research work was to obtain the loss of life of solid insulation in transformers using different available data alternatively. Even though numerous investigators have worked on the transformer loss of life prediction, as shown in Table 1, no existing research has reported on the application of FFANN to suggest each of the methods proposed in the current study. On the two novel approaches proposed in the current work, the first model was based on predicting the DP when only the 2-furaldehyde (2FAL) concentration measured from the oil samples is available for new and existing transformers. The second FFANN model proposed was based on predicting the transformer LOL when the 2FAL and DP are available to the utility owner, typically for the transformer operating at a site where un-tanking the unit will be a daunting and unfeasible task. These approaches are crucial in the development of prediction techniques for the DP and LOL of new and existing transformers at the manufacturer's premises and operating in the field, respectively.

#### *1.3. The Manuscript Organization*

The rest of this manuscript is organized as follows. Section 2 introduces the fundamental principle of artificial neural networks and proposed feedforward artificial neural network. Section 3 presents the results of thee developed models for predicting DP when only 2FAL is available and predicting LOL using the predicted DP and measured 2FAL. Finally, Section 4 presents a detailed conclusion.
