Weighted time series fault diagnosis based on a stacked sparse autoencoder

2017 ◽  
Vol 31 (9) ◽  
pp. e2912 ◽  
Author(s):  
Feiya Lv ◽  
Chenglin Wen ◽  
Meiqin Liu ◽  
Zhejing Bao
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

scholarly journals Reliable Fault Diagnosis of Rotary Machine Bearings Using a Stacked Sparse Autoencoder-Based Deep Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Language Processing ◽  
Classification Performance ◽  
Fault Classification ◽  
Shaft Speed ◽  
Complex Envelope ◽  
The Past ◽  
Feature Extraction And Selection ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Due to enhanced safety, cost-effectiveness, and reliability requirements, fault diagnosis of bearings using vibration acceleration signals has been a key area of research over the past several decades. Many fault diagnosis algorithms have been developed that can efficiently classify faults under constant speed conditions. However, the performances of these traditional algorithms deteriorate with fluctuations of the shaft speed. In the past couple of years, deep learning algorithms have not only improved the classification performance in various disciplines (e.g., in image processing and natural language processing), but also reduced the complexity of feature extraction and selection processes. In this study, using complex envelope spectra and stacked sparse autoencoder- (SSAE-) based deep neural networks (DNNs), a fault diagnosis scheme is developed that can overcome fluctuations of the shaft speed. The complex envelope spectrum made the frequency components associated with each fault type vibrant, hence helping the autoencoders to learn the characteristic features from the given input signals more readily. Moreover, the implementation of SSAE-DNN for bearing fault diagnosis has avoided the need of handcrafted features that are used in traditional fault diagnosis schemes. The experimental results demonstrate that the proposed scheme outperforms conventional fault diagnosis algorithms in terms of fault classification accuracy when tested with variable shaft speed data.

scholarly journals Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Hongmei Liu ◽  
Lianfeng Li ◽  
Jian Ma
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Short Time Fourier Transform ◽  
Bearing Fault Diagnosis ◽  
Fault Features ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Short Time

The main challenge of fault diagnosis lies in finding good fault features. A deep learning network has the ability to automatically learn good characteristics from input data in an unsupervised fashion, and its unique layer-wise pretraining and fine-tuning using the backpropagation strategy can solve the difficulties of training deep multilayer networks. Stacked sparse autoencoders or other deep architectures have shown excellent performance in speech recognition, face recognition, text classification, image recognition, and other application domains. Thus far, however, there have been very few research studies on deep learning in fault diagnosis. In this paper, a new rolling bearing fault diagnosis method that is based on short-time Fourier transform and stacked sparse autoencoder is first proposed; this method analyzes sound signals. After spectrograms are obtained by short-time Fourier transform, stacked sparse autoencoder is employed to automatically extract the fault features, and softmax regression is adopted as the method for classifying the fault modes. The proposed method, when applied to sound signals that are obtained from a rolling bearing test rig, is compared with empirical mode decomposition, Teager energy operator, and stacked sparse autoencoder when using vibration signals to verify the performance and effectiveness of the proposed method.

scholarly journals Stacked Sparse Autoencoder-Based Deep Network for Fault Diagnosis of Rotating Machinery

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 15066-15079 ◽  
Author(s):  
Yumei Qi ◽  
Changqing Shen ◽  
Dong Wang ◽  
Juanjuan Shi ◽  
Xingxing Jiang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Deep Network ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

scholarly journals Translation Invariance-Based Deep Learning for Rotating Machinery Diagnosis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Wenliao Du ◽  
Shuangyuan Wang ◽  
Xiaoyun Gong ◽  
Hongchao Wang ◽  
Xingyan Yao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Domain Knowledge ◽  
Translation Invariance ◽  
Complex Wavelet Transform ◽  
Translational Invariance ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Discriminative feature extraction is a challenge for data-driven fault diagnosis. Although deep learning algorithms can automatically learn a good set of features without manual intervention, the lack of domain knowledge greatly limits the performance improvement, especially for nonstationary and nonlinear signals. This paper develops a multiscale information fusion-based stacked sparse autoencoder fault diagnosis method. The autoencoder takes advantage of the multiscale normalized frequency spectrum information obtained by dual-tree complex wavelet transform as input. Accordingly, the multiscale normalized features guarantee the translational invariance for signal characteristics, and the stacked sparse autoencoder benefits the unsupervised feature learning and ensures accurate and stable diagnosis performance. The developed method is performed on motor bearing vibration signals and worm gearbox vibration signals, respectively. The results confirm that the developed method can accommodate changing working conditions, be free of manual feature extraction, and perform better than the existing intelligent diagnosis methods.

scholarly journals Construction of a Sensitive and Speed Invariant Gearbox Fault Diagnosis Model Using an Incorporated Utilizing Adaptive Noise Control and a Stacked Sparse Autoencoder-Based Deep Neural Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 18
Author(s):  
Cong Dai Nguyen ◽  
Alexander E. Prosvirin ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Noise Control ◽  
Vibration Characteristics ◽  
Vibration Signals ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Diagnosis Model ◽  
Adaptive Noise

Gearbox fault diagnosis based on the analysis of vibration signals has been a major research topic for a few decades due to the advantages of vibration characteristics. Such characteristics are used for early fault detection to guarantee the enhanced safety of complex systems and their cost-effective operation. There exist many fault diagnosis models that have been developed for classifying various fault types in gearboxes. However, the classification results of the conventional fault classification models degrade when they are applied to gearbox systems with multi-level tooth cut gear (MTCG) faults operating under variable shaft speeds. These conditions cause difficulty in discriminating the gear fault types. Due to the improved computational capabilities of modern systems, the application of deep neural networks (DNNs) is getting popular in a variety of research fields, such as image and natural language processing. DNNs are capable of improving the classification results even when addressing complex problems such as diagnosing gearbox MTCG faults. In this research, an adaptive noise control (ANC) and a stacked sparse autoencoder–based deep neural network (SSA-DNN) are used to construct a sensitive fault diagnosis model that can diagnose a gearbox system with MTCG fault types under varying shaft rotation speeds, despite its complicatedness. An ANC is applied to gear vibration characteristics to remove a significant level of noise along the frequency spectrum of vibration signals to fix the most fault-informative components of each fault case. Next, the autoencoder learns the gear faults characteristic features from these fault-informative components to separate the fault types considered in this study. Furthermore, the implementation of the SSA-DNN is substituted for feature extraction, feature selection, and the classification processes in traditional fault diagnosis schemes by high-performance unity. The experimental results show that the proposed model outperforms conventional methodologies with higher classification accuracy.

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