The insulated gate bipolar transistor (IGBT) is a crucial component of power converters (PCVs) and is commonly used in several PCVs topologies. On the other hand, the investigation and the study of the IGBT component show several changes within its behavior and lifetime, while this component is highly influenced by the operating conditions. Indeed, the monitoring of this component is necessary to minimize unexpected downtime of the wind energy system (WES). However, an accurate prediction of IGBTs remaining useful life (RUL) is the key enabler for life-time-optimized operation. Consequently, this work proposes a new prognostic approach for online IGBTs monitoring that adopts the time-domain analysis to extract useful information that is used as an input in the generation of the health indicator. Moreover, this approach is based on combining both of principal component analysis (PCA) technique and the feedforward neural network (FFNN) technique. PCA is used to reduce features extracted from IGBTs and the FFNN is implemented to achieve online regression of the trend parameter obtained from the PCA technique. To investigate and evaluate the performance of our idea we used the NASA Ames Laboratory Prognostics Center of Excellence IGBTs accelerated aging database. Finally, the achieved results clearly show the strength of the new trend parameter for IGBTs RUL prediction. The most notable strong correlation within the proposed approach is in relation to accuracy value, with an acceptable average accuracy rate of 60.4%.

This paper presents a novel approach for smoke detection to overcome forest wildfires based on machine learning technique (Deep Belief Network). Video smoke detection is applied on many surveillance and security applications. Smoke detection method should have a high detection rate to have a strong detector of smoke detection. Deep Belief Network which is a stacked layers of Restricted Boltzman Machine is the technique that we used for smoke detection. This technique extracts and classify smoke and no smoke regions simultaneously. The effectiveness of our implemented smoke detection method is evaluated after calculating smoke detection rate, time of pre-traing and time of fine-tuning. The higher is the detection rate the better is the smoke method and the lowest is the time of pre-training and fine-tuning the speeder is the method for smoke detection.