Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using Deep Belief Networks (DBN). The DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked Restricted Boltzmann Machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using the DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing the sensory data for DBN training and testing; second, developing DBN based classification models for the diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. The performance of health diagnosis using DBN based health state classification is compared with support vector machine technique and demonstrated with aircraft wing structure health diagnostics and aircraft engine health diagnosis using 2008 PHM challenge data. <s> BIB001 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Effective health diagnosis provides multifarious benefits such as improved safety, improved reliability and reduced costs for operation and maintenance of complex engineered systems. This paper presents a novel multi-sensor health diagnosis method using deep belief network (DBN). DBN has recently become a popular approach in machine learning for its promised advantages such as fast inference and the ability to encode richer and higher order network structures. The DBN employs a hierarchical structure with multiple stacked restricted Boltzmann machines and works through a layer by layer successive learning process. The proposed multi-sensor health diagnosis methodology using DBN based state classification can be structured in three consecutive stages: first, defining health states and preprocessing sensory data for DBN training and testing; second, developing DBN based classification models for diagnosis of predefined health states; third, validating DBN classification models with testing sensory dataset. Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques. Benchmark classification problems and two engineering health diagnosis applications: aircraft engine health diagnosis and electric power transformer health diagnosis are employed to demonstrate the efficacy of the proposed approach. <s> BIB002 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Gearboxes are crucial transmission components in mechanical systems. Fault diagnosis is an important tool to maintain gearboxes in healthy conditions. It is challenging to recognize fault existences and, if any, failure patterns in such transmission elements due to their complicated configurations. This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes. Considering that different modalities can be made to describe same object, multimodal homologous features of the gearbox vibration measurements are first separated in time, frequency and wavelet modalities, respectively. A Gaussian-Bernoulli deep Boltzmann machine (GDBM) without final output is subsequently suggested to learn pattern representations for features in each modality. A support vector classifier is finally applied to fuse GDBMs in different modalities towards the construction of the MDSVC model. With the present model, "deep" representations from "wide" modalities improve fault diagnosis capabilities. Fault diagnosis experiments were carried out to evaluate the proposed method on both spur and helical gearboxes. The proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods. Results indicate that the proposed separation-fusion based deep learning strategy is effective for the gearbox fault diagnosis. <s> BIB003 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Abstract Information extracting method from numerous measured signals is a critical technique for intelligent manufacturing application to further reduce the manpower cost and improve the productivity and workpiece quality. Manually defining signal features,as the common way, unfortunately will lose most of the information and the performance can’t be guaranteed. In the past few years, machine learning method with deep structure has been the most promising automatic feature extracting method which has made great breakthrough in computer vision and automatic speech recognition. In this paper, deep belief networksareemployed using vibration signal obtained from endmilling to build feature space for cutting states monitoring. Greedy layer-wise strategy is adopted to pre-train the network and standard samples are used for fine-tuning by applying back-propagation method. Comparisons are made with several manually defined features both in time and frequency domain, like MFCC and wavelet method. Different modeling methods are also employed in the research forcomparisons. Resultsshow that the deep learning method has similar ability to characterize the signal for cutting states monitoring compared to those manually defined features. And the modeling accuracy ismuch better thanother traditionalmodeling methods.Furthermore, benefitting fromthe potentialcapability in information fusion, deep learning method would be a promising solution for more complex applications, like tool wear monitoring, machining surface prediction et al. <s> BIB004 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Identifying gearbox damage categories, especially for early faults and combined faults, is a challenging task in gearbox fault diagnosis. This paper presents multiple classifiers based on multi-layer neural networks (MLNN) to implement vibration signals for fault diagnosis in gearbox. A MLNN-based learning architecture using deep belief network (MLNNDBN) is proposed for gearbox fault diagnosis. Training process of the proposed learning architecture includes two stages: A deep belief network is constructed firstly, and then is trained; after a certain amount of epochs, the weights of deep belief network are used to initialize the weights of the constructed MLNN; at last, the trained MLNN is used as classifiers to classify gearbox faults. Multidimensional feature sets including time-domain, frequency-domain features are extracted to reveal gear health conditions. Experiments with different combined faults were conducted, and the vibration signals were captured under different loads and motor speeds. To confirm the superiority of MLNNDBN in fault classification, its performance is compared with other MLNN-based methods with different fine-tuning schemes and relevant vector machine. The achieved accuracy indicates that the proposed approach is highly reliable and applicable in fault diagnosis of industrial reciprocating machinery. <s> BIB005 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Early diagnosis that can detect faults from some symptoms accurately is critical, because it provides the potential benefits such as reducing maintenance costs, improving productivity and avoiding serious damages. Degradation pattern classification for early diagnosis has not been explored in many researches yet. This paper will use hybrid ensemble model for degradation pattern classification. Supervised training of deep models (e.g. Many-layered Neural Nets) is difficult for optimization problem with unlabeled datasets or insufficient data sample. Shallow models (SVMs, Neural Networks, etc...) are unlikely candidates for learning high-level abstractions, since they are affected by the curse of dimensionality. Therefore, deep learning network (DBN), an unsupervised learning model, in diagnosis problem has been investigated to do classification. Few researches have been done for exploring the effects of DBN in diagnosis. In this paper, an ensemble of DBNs with MOEA/D has been applied for diagnosis to handle failure degradation with multivariate sensory data. Turbofan engine degradation dataset is employed to demonstrate the efficacy of the proposed model. We believe that deep learning with multi-objective ensemble for degradation pattern classification can shed new light on failure diagnosis, and our work presented the applicability of this method to diagnosis as well as prognostics. <s> BIB006 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> In the Internet-of-Things environment, it is critical to bridge the gap between business decision-making and real-time factory data to let companies transfer from condition-based maintenance service to predictive maintenance service. Condition monitoring systems have been widely applied to many industries to acquire operation and equipment related data, through which machine health state can be evaluated. One of the challenges of predicting future machine health lies in extracting the right features that are correlated well with the fault progression/degradation. We propose an enhanced restricted Boltzmann machine with a novel regularization term to automatically generate features that are suitable for remaining useful life prediction. The regularization term tries to maximize the trendability of the output features, which potentially better represent the degradation pattern of a system. The proposed method is benchmarked with regular restricted Boltzmann machine algorithm and principal component analysis. The generated features are used as input to a similarity-based method for life prediction. Run-to-failure datasets collected from two rotating systems are used for validation. <s> BIB007 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for 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> BIB008 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. <s> BIB009 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Abstract Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes. <s> BIB010 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> The most important task of Prognostic and Health Management (PHM) in industrial company is to prevent catastrophic failures and reduce maintenance costs, where degradation assessment plays a significant role in schedule required preventive actions. In this paper, to assess the bearing's performance degradation, a new method which is based on the Weibull distribution and deep belief network is proposed. A health state and five degradation states are determined according to the fitted vibration feature by Weibull distribution, which is used to avoid areas of fluctuation of the statistical parameter. Deep belief network has the ability to model nonlinear time series, so it is used to classify the different states of the bearing. Finally, the experimental cases show that the proposed method has a good performance in degradation assessment. <s> BIB011 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Aiming at automated and intelligent state monitoring of induction motors, which are an integral component of a broad spectrum of manufacturing machines, this paper presents a Deep Belief Network (DBN)-based approach to automatically extract relevant features from vibration signals that characterize the working condition of an induction motor. The DBN model employs a structure with stacked restricted Boltzmann machines (RBMs), and is trained by an efficient learning algorithm called greedy layer-wise training. Vibration signals are used as the input to the DBN, and the outputs from activation functions of the trained network are the features needed for fault diagnosis. Comparing to traditional feature extraction methods for induction motor fault diagnosis such as wavelet packet transform, the proposed method is able to learn features directly from the vibration signal to achieve comparable performance with high classification accuracy. Experiments conducted on a machine fault simulator have verified the effectiveness of the proposed method for induction motor fault diagnosis. <s> BIB012 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods. <s> BIB013 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> Abstract A novel hierarchical diagnosis network (HDN) is proposed by collecting deep belief networks (DBNs) by layer for the hierarchical identification of mechanical system. The deeper layer in HDN presents a more detailed classification of the result generated from the last layer to provide representative features for different tasks. A two-layer HDN is designed for a two-stage diagnosis with the wavelet packet energy feature. The first layer is intended to identify fault types, while the second layer is developed to further recognize fault severity ranking from the result of the first layer. To confirm the effectiveness of HDN, two similar networks constructed by support vector machine and back propagation neuron networks (BPNN) are employed to present a comprehensive comparison. The experimental results show that HDN is highly reliable for precise multi-stage diagnosis and can overcome the overlapping problem caused by noise and other disturbances. <s> BIB014 </s> Deep Learning and Its Applications to Machine Health Monitoring: A Survey <s> B. RBM and its variants for machine health monitoring <s> In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most effective and reliable maintenance policy. Prognostics, as one of the key enablers of CBM, involves the core task of estimating the remaining useful life (RUL) of the system. Neural networks-based approaches have produced promising results on RUL estimation, although their performances are influenced by handcrafted features and manually specified parameters. In this paper, we propose a multiobjective deep belief networks ensemble (MODBNE) method. MODBNE employs a multiobjective evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives. The eventually evolved DBNs are combined to establish an ensemble model used for RUL estimation, where combination weights are optimized via a single-objective differential evolution algorithm using a task-oriented objective function. We evaluate the proposed method on several prognostic benchmarking data sets and also compare it with some existing approaches. Experimental results demonstrate the superiority of our proposed method. <s> BIB015
In the section, some work focused on developing RBM and DBM to learn representation from machinery data. Most of works introduced here are based on deep belief networks (DBN) that can pretrain a deep neural network (DNN). In  , a RBM based method for bearing remaining useful life (RUL) prediction was proposed. Linear regression layer was added at the top of RBM after pretraining to predict the future root mean square (RMS) based on a lagged time series of RMS values. Then, RUL was calculated by using the predicted RMS and the total time of the bearing's life. Liao et al. proposed a new RBM for representation learning to predict RUL of machines BIB007 . In their work, a new regularization term modeling the trendability of the hidden nodes was added into the training objective function of RBM. Then, unsupervised self-organizing map algorithm (SOM) was applied to transforming the representation learned by the enhanced RBM to one scale named health value. Finally, the health value was used to predict RUL via a similarity-based life prediction algorithm. In BIB003 , a multi-modal deep support vector classification approach was proposed for fault diagnosis of gearboxes. Firstly, three modalities features including time, frequency and time-frequency ones were extracted from vibration signals. Then, three Gaussian-Bernoulli deep Boltzmann machines (GDBMS) were applied to addressing the above three modalities, respectively. In each GDBMS, the softmax Illustrations of the proposed SAE-DNN for rotating machinery diagnosis in BIB008 . layer was used at the top. After the pretraining and the finetuning processes, the probabilistic outputs of the softmax layers from these three GDBMS were fused by a support vector classification (SVC) framework to make the final prediction. Li et al. applied one GDBMS directly on the concatenation feature consisting of three modalities features including time, frequency and time-frequency ones and stacked one softmax layer on top of GDBMS to recognize fault categories BIB009 . Li et al. adopted a two-layers DBM to learn deep representations of the statistical parameters of the wavelet packet transform (WPT) of raw sensory signal for gearbox fault diagnosis BIB010 . In this work focusing on data fusion, two DBMs were applied on acoustic and vibratory signals and random forest was applied to fusing the representations learned by these two DBMs. Making use of DBN-based DNN, Ma et al. presented this framework for degradation assessment under a bearing accelerated life test BIB011 . The statistical feature, root mean square (RMS) fitted by Weibull distribution that can avoid areas of fluctuation of the statistical parameter and the frequency domain features were extracted as raw input. To give a clear illustration, the framework in BIB011 is shown in Figure 7 . Shao et al. proposed DBN for induction motor fault diagnosis with the direct usage of vibration signals as input BIB012 . Beside the evaluation of the final classification accuracies, t-SNE algorithm was adopted to visualize the learned representation of DBN and outputs of each layer in DBN. They found the addition of hidden layer can increase the discriminative power in the learned representation. Fu et al. employed deep belief networks for cutting states monitoring BIB004 . In the presented work, three different feature sets including raw vibration signal, Mel-frequency cepstrum coefficient (MFCC) and wavelet features were fed into DBN as three corresponding different inputs, which were able to achieve robust comparative performance on the raw vibration signal without too much feature engineering. Tamilselvan et al. proposed a multi-sensory DBN-based health state classification model. The model was verified in benchmark classification problems and two health diagnosis applications including aircraft engine health diagnosis and electric power transformer health diagnosis BIB002 , BIB001 . Tao et al. proposed DBN based multisensor information fusion scheme for bearing fault diagnosis BIB013 . Firstly, 14 time-domain statistical features extracted from three vibration signals acquired by three sensors were concatenated together as an input vector to DBM model. During pre-training, a predefined threshold value was introduced to determine its iteration number. In BIB005 , a feature vector consisting of load and speed measure, time domain features and frequency domain features was fed into DBN-based DNN for gearbox fault diagnosis. In the work of BIB014 , Gan et al. built a hierarchical diagnosis network for fault pattern recognition of rolling element bearings consisting of two consecutive phases where the four different fault locations (including one health state) were firstly identified and then discrete fault severities in each fault condition were classified. In each phases, the frequency-band energy features generated by WPT were fed into DBN-based DNN for pattern classification. In  , raw vibration signals were pre-processed to generate 2D image based on omnidirectional regeneration (ODR) techniques and then, histogram of original gradients (HOG) descriptor was applied on the generated image and the learned vector was fed into DBN for automatic diagnosis of journal bearing rotor systems. Chen et al. proposed an ensemble of DBNs with multi-objective evolutionary optimization on decomposition algorithm (MOEA/D) for fault diagnosis with multivariate sensory data BIB006 . DBNs with different architectures can be regarded as base classifiers and MOEA/D was introduced to adjust the ensemble weights to achieve a trade-off between accuracy and diversity. Chen et al. then extended this above framework for one specific prognostics task: the RUL estimation of the mechanical system BIB015 .