Deep learning and its applications to machine health monitoring

Applications of machine learning to machine fault diagnosis: A review and roadmap

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

Digital twin is a significant way to achieve smart manufacturing, and provides a new paradigm for fault diagnosis. Traditional data-based fault diagnosis methods mostly assume that the training data and test data are following the same distribution and can acquire sufficient data to train a reliable diagnosis model, which is unrealistic in the dynamic changing production process. In this paper, we present a two-phase digital-twin-assisted fault diagnosis method using deep transfer learning (DFDD), which realizes fault diagnosis both in the development and maintenance phases. At first, the potential problems that are not considered at design time can be discovered through front running the ultra-high-fidelity model in the virtual space, while a deep neural network (DNN)-based diagnosis model will be fully trained. In the second phase, the previously trained diagnosis model can be migrated from the virtual space to physical space using deep transfer learning for real-time monitoring and predictive maintenance. This ensures the accuracy of the diagnosis as well as avoids wasting time and knowledge. A case study about fault diagnosis using DFDD in a car body-side production line is presented. The results show the superiority and feasibility of our proposed method.

/pdf/a-digital-twin-assisted-fault-diagnosis-using-deep-transfer-3nbcseicuk.pdf

A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning

Deep belief network (DBN) is one of the most representative deep learning models. However, it has a disadvantage that the network structure and parameters are basically determined by experiences. In this article, an improved quantum-inspired differential evolution (MSIQDE), namely MSIQDE algorithm based on making use of the merits of the Mexh wavelet function, standard normal distribution, adaptive quantum state update, and quantum nongate mutation, is proposed to avoid premature convergence and improve the global search ability. Then, the MSIQDE with global optimization ability is used to optimize the parameters of the DBN to construct an optimal DBN model, which is further applied to propose a new fault classification, namely MSIQDE-DBN method. Finally, the vibration data of rolling bearings from the Case Western Reserve University and a real-world engineering application are carried out to verify the performance of the MSIQDE-DBN method. The experimental results show that the MSIQDE takes on better optimization performance, and the MSIQDE-DBN can obtain higher classification accuracy than the other comparison methods.

An Improved Quantum-Inspired Differential Evolution Algorithm for Deep Belief Network

Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and diagnostic expertise, and limited capacity for learning complex relationships in fault signals. Furthermore, following the increase in condition data, how to promptly process the massive fault data and automatically provide accurate diagnosis has become an urgent need to solve. Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines. In this paper, a nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain. For exploring the discrimination hidden in the acquired data, a stacked sparse autoencoders-based deep neural network is established and performed with an unsupervised learning procedure followed by a supervised fine-tuning process. We studied the significance of compressed acquisition and provided the effects of key factors and comparison with traditional methods. The effectiveness of the proposed method is validated using data sets from rolling element bearings and the analysis shows that it is able to obtain high diagnotic accuracies and is superior to the existing methods. The proposed method reduces the need of human labor and expertise and provides new strategy to handle the massive data more easily.

Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

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Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning