Deep learning has seen tremendous growth over the past decade. It has set new performance limits for a wide range of applications, including computer vision, speech recognition, and machinery health monitoring. With the abundance of instrumentation data and the availability of high computational power, deep learning continues to prove itself as an efficient tool for the extraction of micropatterns from machinery big data repositories. This study presents a comparative study for feature extraction capabilities using stacked autoencoders considering the use of expert domain knowledge. Case Western Reserve University bearing dataset was used for the study, and a classifier was trained and tested to extract and visualize features from 12 different failure classes. Based on the raw data preprocessing, four different deep neural network structures were studied. Results indicated that integrating domain knowledge with deep learning techniques improved feature extraction capabilities and reduced the deep neural networks size and computational requirements without the need for exhaustive deep neural networks architecture tuning and modification.
Aircraft prototyping and modeling is usually associated with resource expensive techniques and significant post-flight analysis. The NASA Learn-To-Fly concept targets the replacement of the conventional ground-based aircraft development and prototyping approaches with an efficient real-time paradigm. The work presented herein describes a learning paradigm of a quadcopter unmanned aircraft that utilizes real-time flight data. Closed-loop parameter estimation of a highly collinear model terms such as those found on a quadrotor is challenging. Using phase optimized orthogonal multisine input maneuvers, collinearity of flight data decreases leading to fast and accurate convergence of the Fourier transform regression estimator. The generated models are utilized to reconfigure a nonlinear dynamic inversion controller in normal, failure, and learning testing conditions. Results show highly accurate model estimation in different testing scenarios. Additionally, the nonlinear dynamic inversion controller easily integrates the identified model parameters without any need for gain scheduling or computationally expensive methods. Overall, the proposed technique introduces an efficient integration between real-time modeling and control adaptation utilizing the limited computational power of the quadcopter’s microcomputer.
Intelligent fault diagnosis utilizing deep learning algorithms has been widely investigated recently. Although previous results demonstrated excellent performance, features learned by Deep Neural Networks (DNN) are part of a large black box. Consequently, lack of understanding of underlying physical meanings embedded within the features can lead to poor performance when applied to different but related datasets i.e. transfer learning applications. This study will investigate the transfer learning performance of a Convolution Neural Network (CNN) considering 4 different operating conditions. Utilizing the Case Western Reserve University (CWRU) bearing dataset, the CNN will be trained to classify 12 classes. Each class represents a unique differentfault scenario with varying severity i.e. inner race fault of 0.007”, 0.014” diameter. Initially, zero load data will be utilized for model training and the model will be tuned until testing accuracy above 99% is obtained. The model performance will be evaluated by feeding vibration data collected when the load is varied to 1, 2 and 3 HP. Initial results indicated that the classification accuracy will degrade substantially. Hence, this paper will visualize convolution kernels in time and frequency domains and will investigate the influence of changing loads on fault characteristics, network classification mechanism and activation strength.
Data-driven fault diagnosis utilizing deep learning algorithms is currently a topic of great interest. Without proper training, data-driven models usually fail to generalize on operating conditions different from the ones used in the training set. The majority of domain adaptation research for machinery fault diagnosis focuses on the transfer between limited working conditions for the same machine. In real-life applications, machines operate under a wide range of operating conditions and the data are mostly available for healthy conditions with seldom failures. Hence, models generated from controlled experiments do not usually generalize well under substantial domain shifts. To address this issue, this paper proposes a semi-unsupervised domain adaptation approach for cross-machine fault diagnosis which integrates model optimization and Generative Adversarial Networks (GANs) to bridge the gap between source and target domains. Experiments of transferring between two bearing data sets show that the proposed method is able to effectively train an optimized model that generalizes on both the source and target domains, and train a generator that learns the source domain probability distribution to substitute for larger domain shifts.
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