Health condition monitoring of machines based on long short-term memory convolutional autoencoder

Machine health assessment is crucial to prognostics and health management (PHM), which can increase reliability of machines while reducing operation cost. However, the data collected from machines in real-world cases are noised and high-dimensional, so that it is very difficult to detect the early defects of machines. Moreover, it is still a challenging problem to capture sequential information from multi-sensor signals for machine health assessment. This paper proposes a novel autoencoder (AE), called long short-term memory convolutional autoencoder (LSTMCAE), where LSTM and convolutional units are embedded in this specific network for feature learning from sensor signals based on unsupervised-learning. A long short-term memory (LSTM) unit in LSTMCAE is utilized to capture sequential information from multi-sensor time series data. A convolutional and deconvolutional unit is further embedded after the LSTM unit to filter noise and extract features corresponding to health state of machines. Residual learning is employed to reduce the training difficulty and improve the feature learning performance of LSTMCAE. Multivariate Gaussian distribution (MGD) is adopted to generate health index (HI) based on reconstruction errors of LSTMCAE for quantifying machines health state. A contribution analysis-based feature selection method is proposed to select effective features for machinery health assessment. Experimental results on turbofan engines demonstrate the effectiveness of LSTMCAE for machine health assessment. Compared with other unsupervised learning methods, the HIs generated by LSTMCAE show better tendency. LSTMCAE can detect earlier slight degradation than LSTM-AE. Moreover, contribution analysis results indicate that the sensitive variables can be selected for machine health monitoring. Residual learning significantly improves the feature learning performance of LSTMCAE. Thus, LSTMCAE can well quantify the degradation trend of machines. The experimental results on engines show that the proposed method will be an effective tool for machine health assessment.