Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-based maintenance. A major challenge in data-driven prognostics is the difficulty of obtaining a sufficient number of samples of failure progression. However, for traditional machine learning methods and deep neural networks, enough training data is a prerequisite to train good prediction models. In this work, we proposed a transfer learning algorithm based on Bi-directional Long Short-Term Memory (BLSTM) recurrent neural networks for RUL estimation, in which the models can be first trained on different but related datasets and then fine-tuned by the target dataset. Extensive experimental results show that transfer learning can in general improve the prediction models on the dataset with a small number of samples. There is one exception that when transferring from multi-type operating conditions to single operating conditions, transfer learning led to a worse result.

Currently gear fault diagnosis is mainly based on vibration signals with a few studies on acoustic signal analysis. However, vibration signal acquisition is limited by its contact measuring while traditional acoustic-based gear fault diagnosis relies heavily on prior knowledge of signal processing techniques and diagnostic expertise. In this paper, a novel deep learning-based gear fault diagnosis method is proposed based on sound signal analysis. By establishing an end-to-end convolutional neural network (CNN), the time and frequency domain signals can be fed into the model as raw signals without feature engineering. Moreover, multi-channel information from different microphones can also be fused by CNN channels without using an extra fusion algorithm. Our experiment results show that our method achieved much better performance on gear fault diagnosis compared with other traditional gear fault diagnosis methods involving feature engineering. A publicly available sound signal dataset for gear fault diagnosis is also released and can be downloaded as instructed in the conclusion section.

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Protein sorting is an important mechanism for transporting proteins to their target subcellular locations after their synthesis. Mutations on genes may disrupt the well regulated protein sorting process, leading to a variety of mislocation related diseases. This paper proposes a methodology to discover such disease genes based on gene expression data and computational protein localization prediction. A kernel logistic regression based algorithm is used to successfully identify several candidate cancer genes which may cause cancers due to their mislocation within the cell. Our results also showed that compared to the gene co-expression network defined on Pearson correlation coefficients, the nonliear Maximum Correlation Coefficients (MIC) based co-expression network give better results for subcellular localization prediction.

Interactions between human leukocyte antigens (HLAs) and peptides play a critical role 1 in the human immune system. Accurate computational prediction of HLA-binding 2 peptides can be used for peptide drug discovery. Currently, the best prediction