Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> D. RNN for machine health monitoring <s> Prognostics technique aims to accurately estimate the Remaining Useful Life (RUL) of a subsystem or a component using sensor data, which has many real world applications. However, many of the existing algorithms are based on linear models, which cannot capture the complex relationship between the sensor data and RUL. Although Multilayer Perceptron (MLP) has been applied to predict RUL, it cannot learn salient features automatically, because of its network structure. A novel deep Convolutional Neural Network (CNN) based regression approach for estimating the RUL is proposed in this paper. Although CNN has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt CNN for RUL estimation in prognostics. Different from the existing CNN structure for computer vision, the convolution and pooling filters in our approach are applied along the temporal dimension over the multi-channel sensor data to incorporate automated feature learning from raw sensor signals in a systematic way. Through the deep architecture, the learned features are the higher-level abstract representation of low-level raw sensor signals. Furthermore, feature learning and RUL estimation are mutually enhanced by the supervised feedback. We compared with several state-of-the-art algorithms on two publicly available data sets to evaluate the effectiveness of this proposed approach. The encouraging results demonstrate that our proposed deep convolutional neural network based regression approach for RUL estimation is not only more efficient but also more accurate. <s> BIB001 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> D. RNN for machine health monitoring <s> Aero engine is a kind of sophisticated and expensive industrial product. Accurate fault location and Remaining Useful Life (RUL) estimation for aero engine can lead to appropriate maintenance actions to avoid catastrophic failures and minimize economic losses. The aim of this paper is to propose utilizing Long Short-Term Memory (LSTM) neural network to get good diagnosis and prediction performance in the cases of complicated operations, hybrid faults and strong noises. The whole proposition is demonstrated and discussed by carrying out tests on a health monitoring dataset of aircraft turbofan engines provided by NASA. Performances of LSTM and some of its modifications were tested and contrasted. Experiment results show the standard LSTM outperformed others. <s> BIB002 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> D. RNN for machine health monitoring <s> Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g., exponential degradation. However, in many domains degradation may not follow a pattern. We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is used to compute HI which is then used for RUL estimation. We evaluate our approach on publicly available Turbofan Engine and Milling Machine datasets. We also present results on a real-world industry dataset from a pulverizer mill where we find significant correlation between LSTM-ED based HI and maintenance costs. <s> BIB003 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> D. RNN for machine health monitoring <s> In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classiﬁcation and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redeﬁne representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM ﬁrstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods. <s> BIB004
The majority of machinery data belong to sensor data, which are in nature time series. RNN models including LSTM and BIB001 Input[27 GRU have emerged as one kind of popular architectures to handle sequential data with its ability to encode temporal information. These advanced RNN models have been proposed to relief the difficulty of training in vanilla RNN and applied in machine health monitoring recently. In BIB002 , Yuan et al. investigated three RNN models including vanilla RNN, LSTM and GRU models for fault diagnosis and prognostics of aero engine. They found these advanced RNN models LSTM and GRU models outperformed vanilla RNN. Another interesting observation was the ensemble model of the above three RNN variants did not boost the performance of LSTM. Zhao et al. presented an empirical evaluation of LSTMs-based machine health monitoring system in the tool wear test  . The applied LSTM model encoded the raw sensory data into embeddings and predicted the corresponding tool wear. Zhao et al. further designed a more complex deep learning model combining CNN and LSTM named Convolutional Bidirectional Long Short-Term Memory Networks (CBLSTM) BIB004 . As shown in Figure 9 , CNN was used to extract robust local features from the sequential input, and then bi-directional LSTM was adopted to encode temporal information on the sequential output of CNN. Stacked fully-connected layers and linear regression layer were finally added to predict the target value. In tool wear test, the proposed model was able to outperform several state-of-the-art baseline methods including conventional LSTM models. In BIB003 , Malhotra proposed a very interesting structure for RUL prediction. They designed a LSTM-based encoder-decoder structure, which LSTM-based encoder firstly transforms a multivariate input sequence to a fixed-length vector and then, LSTM decoder uses the vector to produce the target sequence. When it comes to RUL prediction, their assumptions lies that the model can be firstly trained in raw signal corresponding to normal behavior in an unsupervised way. Then, the reconstruction error can be used to compute health index (HI), which is then used for RUL estimation. It is intuitive that the large reconstruction error corresponds to a more unhealthy machine condition.