Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment

Liao, Linxia; Jin, Wenjing; Pavel, Radu

doi:10.1109/tie.2016.2586442

Cited by 226 publications

(80 citation statements)

References 12 publications

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“…Since 2015, deep learning methodologies have been applied, with success, to diagnostics or classification tasks of rolling element signals [2,[16][17][18][19][20][21][22][23][24][25][26]. Wang et al [2] proposed the use of wavelet scalogram images as an input into a CNN to detect faults within a set of vibration data.…”

Section: Introductionmentioning

confidence: 99%

“…Although not dealing with rolling elements, Zhang [22] used a deep learning multiobjective deep belief network ensemble method to estimate the remaining useful life of NASA's C-MAPSS data set. Liao et al [23] used restricted Boltzmann machines (RBMs) as a feature extraction method, otherwise known as transfer learning. Feature selection was completed from the RBM output, followed by a health assessment via selforganizing maps (SOMs).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

265

187

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Traditional feature extraction and selection is a labor-intensive process requiring expert knowledge of the relevant features pertinent to the system. This knowledge is sometimes a luxury and could introduce added uncertainty and bias to the results. To address this problem a deep learning enabled featureless methodology is proposed to automatically learn the features of the data. Timefrequency representations of the raw data are used to generate image representations of the raw signal, which are then fed into a deep convolutional neural network (CNN) architecture for classification and fault diagnosis. This methodology was applied to two public data sets of rolling element bearing vibration signals. Three time-frequency analysis methods (short-time Fourier transform, wavelet transform, and Hilbert-Huang transform) were explored for their representation effectiveness. The proposed CNN architecture achieves better results with less learnable parameters than similar architectures used for fault detection, including cases with experimental noise.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Verstraete

Ferrada

Droguett

et al. 2017

Shock and Vibration

265

187

View full text Add to dashboard Cite

show abstract

“…In addition, we have provided an explanatory tree diagram of this study (see Figure 1), and the organized literature is listed in Table 1. DL (51): RNN [4][5][6][7][8][9][10][11][12][13][14], CNN [15][16][17][18][19][20][21][22][23][24][25][26][27][28], DNN [29][30][31][32][33], RBM [34][35][36], others [1][2][3][37][38][39][40][41][42][43][44][45][46][47][48][49]…”

Section: Brief Introductionmentioning

confidence: 99%

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

2018

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This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.Keywords: artificial intelligence; smart machine tools; learning algorithms; intelligent manufacturing; fault diagnosis and prognosis Brief IntroductionWe believe that a new epoch of the "Industrial Internet of Things (IIoT) plus artificial intelligence (AI)", characterized by big machinery data, data-driven techniques, ubiquitous networks, mass innovation, automatic intelligence, cross-border integration, and shared services, has arrived [1-3]. The fast development and combination of new AI and energy technologies, for materials, bioscience, the Internet, and new-generation information exchange, is a fundamental part of this new epoch. This will, in turn, permit game-changing transformation of models, ecosystems, and means in the light of their application to national security, well-being, and the economy. The main objective is a review and summary of recent achievement in data-based techniques, especially for complicated industrial applications, offering reference for further study from both an academic and practical point of view. Yin et al.[1] describes a brief evolutionary overview of data-based techniques over the last two decades. Recent development of modern industrial applications is presented mainly from the perspectives of monitoring and control. Their methodology, based on process measurements and model-data integrated techniques, will be introduced in the next study. Jeschke et al. [2] developed the core system science needed to enable the development of complex IIoT/manufacturing cyber-physical systems (CPS). Moreover, readers can learn the current state of IIoT and the concept of cybermanufacturing from this book. In 2014, Lund et al. [3] described the central issues contributing to, and characterizing, the worldwide and regional growth of the IoT. Besides, researchers can utilize the trend analysis of IoT their region markets in the future.There are many AI algorithms for machine health monitoring and other machine tool applications: The second-order recurrent neural networks (RNN) method for the learning and extraction of finite

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“…Thus, it is unsuitable for big data [14][15][16]. The data-driven models [15], such as Artificial Neural Networks (ANN) [17,18], Autoencoders [5], Restricted Boltzmann Machine (RBM) [19], Convolutional Neural Networks (CNN) [20,21], and k-Nearest Neighbor [22], depend less on human knowledge and can effectively diagnose faults in mechanical big data. However, these intelligent fault diagnosis methods pose specific challenges, e.g., a difficulty in adjusting various hyperparameters, and good diagnosis accuracy can be obtained only when the hyperparameters are set properly.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

Wang

et al. 2018

Applied Sciences

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Faults in bearings and gearboxes, which are widely used in rotating machines, can lead to heavy investment and productivity loss. Accordingly, a fault diagnosis system is necessary to ensure a high-performance transmission. However, as mechanical fault diagnosis enters the era of big data, it can be difficult to apply traditional fault diagnosis methods because of the massive computation cost and excessive reliance on human labor. Meanwhile, unsupervised learning has been shown to have excellent performance in processing machanical big data. In this paper, an unsupervised learning method known as sparse filtering is applied, the multi-correlation of a weight matrix is investigated, and a method that is more suitable for the feature extraction of signals is proposed. The main contribution of our work is the modification of original method. First, to understand the non-monotonicity testing accuracies of the original method, the physical interpretation of input dimensions is studied. Second, using the physical interpretation, an overfitting phenomenon is discovered and examined. Third, to reduce the overfitting phenomenon, a method which eliminates the multi-correlation of the weight matrix is proposed. Finally, bearing and gear datasets are employed to verify the effectiveness of the proposed method; experimental results show that the proposed method can achieve a superior performance in comparison to the original sparse filtering model.

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Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment

Cited by 226 publications

References 12 publications

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings

A Review of Artificial Intelligence Algorithms Used for Smart Machine Tools

An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation

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