Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> We have developed an analog-circuit fault diagnostic system based on backpropagation neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. The proposed system has the capability to detect and identify faulty components in an analog electronic circuit by analyzing its impulse response. Using wavelet decomposition to preprocess the impulse response drastically reduces the number of inputs to the neural network, simplifying its architecture and minimizing its training and processing time. The second preprocessing by principal component analysis can further reduce the dimensionality of the input space and/or select input features that minimize diagnostic errors. Input normalization removes large dynamic variances over one or more dimensions in input space, which tend to obscure the relevant data fed to the neural network. A comparison of our work with that of Spina and Upadhyaya (see ibid., vol. 44, p. 188-196, 1997), which also employs backpropagation neural networks, reveals that our system requires a much smaller network and performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques. <s> BIB001 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Abstract A procedure is presented for fault diagnosis of rolling element bearings through artificial neural network (ANN). The characteristic features of time-domain vibration signals of the rotating machinery with normal and defective bearings have been used as inputs to the ANN consisting of input, hidden and output layers. The features are obtained from direct processing of the signal segments using very simple preprocessing. The input layer consists of five nodes, one each for root mean square, variance, skewness, kurtosis and normalised sixth central moment of the time-domain vibration signals. The inputs are normalised in the range of 0.0 and 1.0 except for the skewness which is normalised between −1.0 and 1.0. The output layer consists of two binary nodes indicating the status of the machine—normal or defective bearings. Two hidden layers with different number of neurons have been used. The ANN is trained using backpropagation algorithm with a subset of the experimental data for known machine conditions. The ANN is tested using the remaining set of data. The effects of some preprocessing techniques like high-pass, band-pass filtration, envelope detection (demodulation) and wavelet transform of the vibration signals, prior to feature extraction, are also studied. The results show the effectiveness of the ANN in diagnosis of the machine condition. The proposed procedure requires only a few features extracted from the measured vibration data either directly or with simple preprocessing. The reduced number of inputs leads to faster training requiring far less iterations making the procedure suitable for on-line condition monitoring and diagnostics of machines. <s> BIB002 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. Model-based methods are efficient monitoring systems for providing warning and predicting certain faults at early stages. However, the conventional methods must work with explicit motor models, and cannot be applied effectively for vibration signal diagnosis due to their nonadaptation and the random nature of vibration signal. In this paper, an analytical redundancy method using neural network modeling of the induction motor in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform is used to process the quasi-steady vibration signals to continuous spectra for the neural network model training. The faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results, and it is shown that a robust and automatic induction machine condition monitoring system has been produced <s> BIB003 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Support Vector Machine (SVM) has been very popular for use in machine fault diagnosis as classifier. In most of the complex machine learning problems, the main challenge lies in finding good features. Sparse autoencoders have the ability to learn good features from the input data in an unsuperivised fashion. Sparse auto-encoders and other deep architectures are already showing very good results in text classification, speaker and speech recognition and face recognition as well. In this paper, we compare the performance of sparse autoencoders with soft max regression, fast classifier based on Mahalanobis distance and SVM in fault diagnosis of air compressors. <s> BIB004 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> As bearings are the most common components of mechanical structure, it will be helpful to research bearing fault and diagnose the fault as early as possible in case of suffering greater losses. This paper proposes a deep neural network algorithm framework for bearing fault diagnosis based on stacked autoencoder and softmax regression. The simulation results verify the feasibility of the algorithm and show the excellent classification performance. In addition, this deep neural network represents strong robustness and eliminates the impact of noise remarkably. Last but not least, an integrated deep neural network method consisting of ten different structure parameter networks is proposed and it has better generalization capability. <s> BIB005 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Considering the nonlinear and non-stationary characteristics of fault vibration signal in the roller bearing system, an intelligent fault diagnosis model based on wavelet transform and stacked auto-encoder is proposed. This paper firstly uses the combination of digital wavelet frame (DWF) and nonlinear soft threshold method to de-noise fault vibration signal. Then stacked auto-encoder is taken to extract the fault signal feature, which is regarded as the input of BP network classifier. The output results of BP network classifier represent fault categories. In addition, neural network ensemble method is also adopted to greatly improve the recognition rate of fault diagnosis. <s> BIB006 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> The fault diagnosis of hydraulic pump is always a challenging issue in the field of machinery fault diagnosis. Not only is manual feature extraction time-consuming and laborious, but also the diagnostic result is easily affected by subjective experience. The stacked autoencoders (SAE) which has powerful learning and representation ability is applied in hydraulic pump fault diagnosis, and it is directly used for training and identification based on vibration signals, so that we don't need to extract features manually. To avoid gradient vanishing and to improve the performance for small training set, ReLU activation function and Dropout strategy are both introduced into SAE. Validated by experiments, proposed SAE is superior to the BP, SVM and traditional SAE. And it can recognize hydraulic pump condition accurately even if the training set is small, which satisfies the engineering application. <s> BIB007 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> According to the large variety of data generated during the spacecraft test and fault diagnosis, this paper designs a multi class classification algorithm based on deep learning method. The algorithm uses the stack auto-Encoder to initialize the initial weights and offsets of the multi-layer neural network, and then monitor the parameters after the initialization with the gradient descent method. The algorithm can overcome many weaknesses of SVM, for example, it is too complex and occupied more space when data is large or the categories are huge. By studying the measured data, the expert knowledge can be provided for the spacecraft health management platform. Experimental data show that this depth learning algorithm can get a high accuracy in the classification of multi class signals of spacecraft. <s> BIB008 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Rolling bearing fault diagnosis has received much attention because of its importance for the rotatory machinery. Feature extraction is the crucial part of rolling bearing fault diagnosis, which determines the diagnosis performance greatly. However, features extracted by many available methods cannot guarantee the sensitiveness to every interested fault category, which leads to incomplete diagnosis results and ability absence of handling with the situation that unknown-category fault appears. To solve this issue, the feature extraction method based on deep neural network (DNN) is proposed to extract a meaningful representation for bearing signal in this article. DNN is a new kind of machine learning tool with strong power of representation, which has been utilized as the feature extractors in lots of practical applications successfully. Afterwards, the effectiveness of this proposed approach is presented by using the actual rolling bearing data. <s> BIB009 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Abstract This paper presents a deep neural network (DNN) approach for induction motor fault diagnosis. The approach utilizes sparse auto-encoder (SAE) to learn features, which belongs to unsupervised feature learning that only requires unlabeled measurement data. With the help of the denoising coding, partial corruption is added into the input of the SAE to improve robustness of feature representation. Features learned from the SAE are then used to train a neural network classifier for identifying induction motor faults. In addition, to prevent overfitting during the training process, a recently developed regularization method called “dropout” which has been proved to be very effective in neural network was employed. An experiment performed on a machine fault simulator indicates that compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis. <s> BIB010 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Abstract Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods. <s> BIB011 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method. <s> BIB012 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Tidal power is an emerging field of renewable energy, harnessing the power of regular and predictable tidal currents. However, maintenance of submerged equipment is a major challenge. Routine visual inspections of equipment must be performed onshore, requiring the costly removal of turbines from the sea bed and resulting in long periods of downtime. The development of condition monitoring techniques providing automated fault detection can therefore be extremely beneficial to this industry, reducing the dependency on inspections and allowing maintenance to be planned efficiently. This paper investigates the use of deep learning approaches for fault detection within a tidal turbine's generator from vibration data. Training and testing data were recorded over two deployment periods of operation from an accelerometer sensor placed within the nacelle of the turbine, representing ideal and faulty responses over a range of operating conditions. The paper evaluates a deep learning approach through a stacked autoencoder network in comparison to feature-based classification methods, where features have been extracted over varying rotation speeds through the Vold-Kalma filter. <s> BIB013 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition. <s> BIB014 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Recent advancements in sensor technologies and data driven model based techniques have made intelligent diagnostic systems prominent in machine maintenance frameworks of industries. The performance of such systems immensely relies upon the quality of features extracted and the classifier model learned. Traditionally features were handcrafted, where engineers would manually design them with statistical parameters and signal transforms based energy distribution analysis. Recently, deep learning techniques have shown new ways of obtaining useful feature representation that provide state of the art results in image and speech processing applications. This paper first presents a brief survey of traditional handcrafted features and later presents a short analysis of handcrafted features v/s features learned by deep neural networks (DNN), for doing fault diagnosis. The DNN based features in this paper were generated in 3 phases: 1) extracted handcrafted features using traditional techniques 2) initialized the weights of DNN by learning de-noising sparse auto-encoders with the handcrafted features in unsupervised fashion and 3) applied two generic fine tuning heuristics that tailor DNN's weights to give good classification performance. The experimentation and analysis were performed on 5 datasets: one each on Air compressor monitoring, Drill bit monitoring and Steel plate monitoring, and two on bearing fault monitoring data. The results clearly show the prospects of DNN obtaining good feature representations and good classification performance. Further, it also finds that Fast Fourier Transform based features with DNN are more suited for Support Vector Machine as classifier than Random Forest. <s> BIB015 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis. <s> BIB016 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Effective fault diagnosis has long been a research topic in the prognosis and health management of rotary machinery engineered systems due to the benefits such as safety guarantees, reliability improvements, and economical efficiency. This paper investigates an effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations. SDA has become a popular approach to achieve the promised advantages of deep architecture-based robust feature representations. In this paper, the SDA-based fault diagnosis method contains three successive steps: health states are first divided into training and testing groups for the SDA model, a deep hierarchical structure is then established with a transmitting rule of greedy training, layer by layer, where sparsity representation and data destruction are applied to obtain high-order characteristics with better robustness in the iteration learning. Validation data are finally employed to confirm the fault diagnosis results of the SDA, where existing health state identification methods are used for comparison. Rotating machinery datasets are employed to demonstrate the effectiveness of the proposed method. Deep neural network is developed for fault diagnosis of typical dynamic systems.Better robustness is achieved under various working conditions and ambient noise.The method helps salient fault characteristic mining and intelligent diagnosis.Validity of the SDA is verified via comparative experiments. <s> BIB017 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> III. APPLICATIONS OF DEEP LEARNING IN MACHINE HEALTH MONITORING <s> Intelligent fault-diagnosis methods using machine learning techniques like support vector machines and artificial neural networks have been widely used to distinguish bearings’ health condition. However, though these methods generally work well, they still have two potential drawbacks when facing massive fault data: (1) the feature extraction process needs prior domain knowledge, and therefore lacks a universal extraction method for various diagnosis issues, and (2) much training time is generally needed by the traditional intelligent diagnosis methods and by the newly presented deep learning methods. In this research, inspired by the feature extraction capability of auto-encoders and the high training speed of extreme learning machines (ELMs), an auto-encoder-ELM-based diagnosis method is proposed for diagnosing faults in bearings to overcome the aforementioned deficiencies. This paper performs a comparative analysis of the proposed method and some state-of-the-art methods, and the experimental results o... <s> BIB018
The conventional multilayer perceptron (MLP) has been applied in the field of machine health monitoring for many years BIB003 ,  , BIB002 , BIB001 . The deep learning techniques have recently been applied to a large number of machine health monitoring systems. The layer-by-layer pretraining of deep neural network (DNN) based on Auto-encoder or RBM can facilitate the training of DNN and improve its discriminative power to characterize machinery data. Convolution neural network and recurrent neural networks provide more advanced and complex compositions mechanism to learn representation from machinery data. In these DL-based MHMS systems, the top layer normally represents the targets. For diagnosis where targets are discrete values, softmax layer is applied. For prognosis with continuous targets, liner regression layer is added. What is more, the end-to-end structure enables DLbased MHMS to be constructed with less human labor and expert knowledge, therefore these models are not limited to specific machine specific or domain. In the following, a brief survey of DL-based MHMS are presented in these above four DL architectures: AE, RBM, CNN and RNN. A. AE and its variants for machine health monitoring AE models, especially stacked DA, can learn high-level representations from machinery data in an automatic way. Sun et al. proposed a one layer AE-based neural network to classify induction motor faults BIB010 . Due to the limited size of training data, they focused on how to prevent overfiting. Not only the number of hidden layer was set to 1, but also dropout technique that masks portions of output neurons randomly was applied on the hidden layer. However, most of proposed models are based on deep architectures by stacking multiple auto-encoders. Lu et al. presented a detailed empirical study of stacked denoising autoencoders with three hidden layers for fault diagnosis of rotary machinery components BIB017 . Specifically, in their experiments including single working condition and cross working conditions, the effectiveness of the receptive input size, deep architecture, sparsity constraint and denosing operation in the SDA model were evaluated. In BIB005 , different structures of a two-layer SAE-based DNN were designed by varying hidden layer size and its masking probability, and evaluated for their performances in fault diagnosis. In these above works, the input features to AE models are raw sensory time-series. Therefore, the input dimensionality is always over hundred, even one thousand. The possible high dimensionality may lead to some potential concerns such as heavy computation cost and overfiting caused by huge model parameters. Therefore, some researchers focused on AE models built upon features extracted from raw input. Jia et al. fed the frequency spectra of time-series data into SAE for rotating machinery diagnosis BIB011 , considering the frequency spectra is able to demonstrate how their constitutive components are distributed with discrete frequencies and may be more discriminative over the health conditions of rotating machinery. The corresponding framework proposed by Jia et al. is shown in Figure 6 . Tan et al. used digital wavelet frame and nonlinear soft threshold method to process the vibration signal and built a SAE on the preprocessed signal for roller bearing fault diagnosis BIB006 . Zhu et al. proposed a SAEbased DNN for hydraulic pump fault diagnosis with input as frequency domain features after Fourier transform BIB007 . In experiments, ReLU activation and dropout technique were analyzed and experimental results have shown to be effective in preventing gradient vanishing and overfiting. In the work presented in BIB012 , the normalized spectrogram generated by STFT of sound signal was fed into two-layers SAE-based DNN for rolling bearing fault diagnosis. Galloway et al. built a two layer SAE-based DNN on spectrograms generated from raw vibration data for tidal turbine vibration fault diagnosis BIB013 . A SAE-based DNN with input as principal components of data extracted by principal component analysis was proposed for spacecraft fault diagnosis in BIB008 . Multi-domain statistical features including time domain features, frequency domain features and time-frequency domain features were fed into the SAE framework, which can be regarded as one kind of feature fusion BIB014 . Similarly, Verma et al. also used these three domains features to fed into a SAE-based DNN for fault diagnosis of air compressors BIB004 . Except that these applied multi-domain features, multisensory data are also addressed by SAE models. Reddy utilized SAE to learn representation on raw time series data from multiple sensors for anomaly detection and fault disambiguation in flight data. To address multi-sensory data, synchronized windows were firstly traversed over multi-modal time series with overlap, and then windows from each sensor were concatenated as the input to the following SAE  . In  , SAE was leveraged for multi-sensory data fusion and the followed DBN was adopted for bearing fault diagnosis, which achieved promising results. The statistical features in time domain and frequency domain extracted from the vibration signals of different sensors were adopted as input to a twolayer SAE with sparsity constraint neural networks. And the learned representation were fed into a deep belief network for pattern classification. In addition, some variants of the conventional SAE were proposed or introduced for machine health monitoring. In BIB015 , Thirukovalluru et al. proposed a two-phase framework that SAE only learn representation and other standard classifiers such as SVM and random forest perform classification. Specifically, in SAE module, handcrafted features based on FFT and WPT were fed into SAE-based DNN. After pretraining and supervised fine-tuning which includes two separated procedures: softmax-based and Median-based fine-tuning methods, the extensive experiments on five datasets including air compressor monitoring, drill bit monitoring, bearing fault monitoring and steel plate monitoring have demonstrated the generalization capability of DL-based machine health monitoring systems. Wang et al. proposed a novel continuous sparse auto-encoder (CSAE) as an unsupervised feature learning for transformer fault recognition BIB016 . Different from conventional sparse AE, their proposed CSAE added the stochastic unit into activation function of each visible unit as: where s j is the output corresponding to the input x i , w ij and a i denote model parameters, ϕ j represents the activation function and the last term σN j (0, 1)) is the added stochastic unit, which is a zero-mean Gaussian with variance σ 2 . The incorporation of stochastic unit is able to change the gradient direction and prevent over-fitting. Mao et al. adopted a variant of AE named Extreme Learning Machine-based auto-encoder for bearing fault diagnosis, which is more efficient than conventional SAE models without sacrificing accuracies in fault diagnosis BIB018 . Different from AE that is trained via back-propagation, the transformation in encoder phase is randomly generated and the one in decoder phase is learned in a single step via leastsquares fit [64] . In addition, Lu et al. focused on the visualization of learned representation by a two-layer SAE-based DNN, which provides a novel view to evaluate the DL-based MHMS BIB009 . In their paper, the discriminative power of learned representation can be improved with the increasing of layers.