In nuclear power plants (NPPs), it is important to ensure the validity of signals for safe operation. However, signals can be corrupted by aging and environmental factors. Thus, active research in signal verification and restoration is required. Previous signal failure detection studies have typically treated any anomalous data as a signal failure and proceeded with restoration. Because these studies targeted only signal failure data, this approach was taken. However, true abnormal situations will not be recognized if unlearned actual abnormal data is treated as a signal failure and restored. Therefore, it is necessary to distinguish between signal failures and abnormal situations. In this study, an algorithm was proposed to distinguish between signal failures and abnormal situations. The algorithm was implemented using an autoencoder (AE) and a long short-term memory (LSTM)-AE. Data from the compact nuclear simulator (CNS) was used for training and testing. The results demonstrated that the proposed algorithm effectively distinguished between signal failures and abnormal situations. Additionally, the LSTM-AE performed better compared to the AE.