Failure Classified Method for Diesel Generators Based Long Short-Term Memory Approach

Deep learning technologies have emerged as optimum solutions for many industrial applications, especially failure-classified of rotating machine problems. The deep learning approach can automatically identify failure types and provide recommendations for unsafe conditions of diesel generators. This research proposed the failure diagnosis method based on long short-term memory to provide the maintenance indicators based on different collected variables. The accumulated datasets in the laboratory evaluate the model capability of failure classified with various evaluating metrics. The classified accuracy is compared with the traditional recurrent neural network (RNN), which demonstrates the classified ability to accurately identify various failure types of diesel generators in the fourth industrial revolution.