In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with deep learning (DL) algorithms. While conventional machine learning (ML) methods, including artificial neural network, principal component analysis, support vector machines, etc., have been successfully applied to the detection and categorization of bearing faults for decades, recent developments in DL algorithms in the last five years have sparked renewed interest in both industry and academia for intelligent machine health monitoring. In this paper, we first provide a brief review of conventional ML methods, before taking a deep dive into the state-of-the-art DL algorithms for bearing fault applications. Specifically, the superiority of DL based methods are analyzed in terms of fault feature extraction and classification performances; many new functionalities enabled by DL techniques are also summarized. In addition, to obtain a more intuitive insight, a comparative study is conducted on the classification accuracy of different algorithms utilizing the open source Case Western Reserve University (CWRU) bearing dataset. Finally, to facilitate the transition on applying various DL algorithms to bearing fault diagnostics, detailed recommendations and suggestions are provided for specific application conditions. Future research directions to further enhance the performance of DL algorithms on health monitoring are also discussed.

pdf/deep-learning-algorithms-for-bearing-fault-diagnosticsx-a-4n8rkcrgab.pdf

Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review

Accurate and timely prediction of remaining useful life (RUL) of a machine enables the machine to have an appropriate operation and maintenance decision. Data-driven RUL prediction methods are more attractive to researchers because they can be deployed quicker and cheaper compared to other approaches. The existing deep neural network (DNN) models proposed for the applications of RUL prediction are mostly single-path and top-down propagation. In order to improve the prognostic accuracy of the network, this paper proposes a directed acyclic graph (DAG) network that combines long short term memory (LSTM) and a convolutional neural network (CNN) to predict the RUL. Different from the existing prediction models combined with CNN and LSTM, the method proposed in this paper combines CNN and LSTM organically instead of just using CNN for feature extraction. Moreover, when a single timestamp is used as an input, padding the signals in the same training batch would affect the prediction ability of the developed model. To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability.

/pdf/a-directed-acyclic-graph-network-combined-with-cnn-and-lstm-14z05m6jd6.pdf

A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction

A BiGRU method for remaining useful life prediction of machinery

As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.

Critical Wind Turbine Components Prognostics: A Comprehensive Review

Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion

Centrifugal pumps with bearings are widely used in nuclear power plants. A challenge in data-driven prognostic technologies for centrifugal pump bearings is to evaluate their degradation features, which can be combined into degradation trajectory; however, the massive data gathered continuously from condition monitoring systems has created challenges to extracting degradation features effectively. The traditional degradation feature extraction methods are highly reliant on prior knowledge and diagnostic expertise, have limited capacities for learning the complex relationships between the degradation features and the massive amounts of measurement data, and their feature design processes are not automated and require intensive human labor. Deep neural networks offer remarkable abilities to extract features from massive data, and they can be used to automatically extract highly abstracted features that correlate well with bearing degradation. Therefore, to extract centrifugal pump bearing degradation features from massive amounts of vibration data, this paper proposes a deep feature optimization fusion method. First, data from the vibration-frequency-domain are mapped to a nonlinear spatial domain using an enhanced autoencoder. Second, the neural nodes in the last hidden layer of the enhanced autoencoder are divided into several child modules. The minimum quantization errors of each child module are used as the candidate degradation features. Finally, the optimal degradation trajectory is obtained via a weighted fusion of the candidate degradation features. The optimal weighting coefficients are calculated using the grey wolf optimizer algorithm. In this paper, experiments were performed using the IEEE PHM2012 bearing prognostic data set and a centrifugal pump bearing condition-monitoring data set. The results demonstrate that the degradation trajectory obtained using the proposed method offers stronger predictive capabilities than do those obtained using other methods, thereby improving the accuracy of predictions of the bearings’ remaining useful life.

A Deep Feature Optimization Fusion Method for Extracting Bearing Degradation Features

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A Deep Feature Optimization Fusion Method for Extracting Bearing Degradation Features