Auto-encoder based deep learning for surface electromyography signal processing

© 2018 Advances in Science, Technology and Engineering Systems. All Rights Reserved. Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages) to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU) will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF) for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and Analysis of Variance will be estimated for different classifiers. Classifier fusion layer will be implemented to glean the classifier which will progress the best accuracies' values for both testing and training to develop the results. Classifier fusion layer brought an encouraging value for both accuracies either training or testing ones

Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for the repair and maintenance of machinery and processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery. First, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states. Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality. Finally, the fruit fly optimization algorithm is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data. The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit. Experimental results show that the proposed method is superior to other state-of-the-art methods

Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction

Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi’an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues

Comparison of new anomaly detection technique for wind turbine condition monitoring using gearbox SCADA data

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O&M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE

Diagnosis of tidal turbine vibration data through deep neural networks

Tidal power is an emerging field of renewable energy, harnessing the power of regular and predictable tidal currents. However, maintenance of submerged equipment is a major challenge. Routine visual inspections of equipment must be performed onshore, requiring the costly removal of turbines from the sea bed and resulting in long periods of downtime. The development of condition monitoring techniques providing automated fault detection can therefore be extremely beneficial to this industry, reducing the dependency on inspections and allowing maintenance to be planned efficiently. This paper investigates the use of deep learning approaches for fault detection within a tidal turbine's generator from vibration data. Training and testing data were recorded over two deployment periods of operation from an accelerometer sensor placed within the nacelle of the turbine, representing ideal and faulty responses over a range of operating conditions. The paper evaluates a deep learning approach through a stacked autoencoder network in comparison to feature-based classification methods, where features have been extracted over varying rotation speeds through the Vold-Kalma filter