Fault diagnosis of diesel generator set based on deep believe network

As a kind of power supply equipment, diesel generator set has the characteristics of good mobility, fast start, stable power supply, convenient operation and maintenance. Diesel generator set is very important for power supply applications. The research on automatic fault diagnosis of diesel generator set is of great significance for monitoring the operation status of diesel generator and timely maintenance. Compared with traditional neural networks, deep believe network improves the learning efficiency of multi-layer networks by introducing restricted Boltzmann machine. A deep believe network based fault diagnosis for diesel generator set is developed. The sensor data collected from diesel generator set are processed to form a training dataset, and deep believe network is designed. The experimental results show that the deep believe network based method has the best fault diagnosis performance in recall, precision, accuracy and F1-score than other learning based methods.