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Fault Diagnosis for Marine Main Engines using Improved Semi-Supervised Locally Linear Embedding

As an important rotating machinery, the frequency domain characteristics of the vibration signals of the marine main engine are important basis for fault diagnosis. In order to improve the speed and accuracy of fault diagnosis as much as possible, features need to be screened. Considering the nonlinear correlation among the dimensions, we use local linear embed-ding(LLE) to reduce dimensions. Some known fault types of data can provide us with more information, which help improve LLE to semi-supervised LLE(SSLLE), but different scales of different features and of different fault types limit the accuracy of traditional SSLLE. Aiming at this problem, we put forward an improved SSLLE algorithm, and have tested it on actual data of ships, obtaining better performance than traditional ways.