Fault diagnosis of rotating machinery under time-varying speed based on order tracking and deep learning

Rotating machinery usually suffers from a type of fault, where the fault feature extracted in the frequency domain is significant, while the fault feature extracted in the time domain is insignificant. For this type of fault, a deep learning-based fault diagnosis method developed in the frequency domain can reach high accuracy performance without real-time performance, whereas a deep learning-based fault diagnosis method developed in the time domain obtains real-time diagnosis with lower diagnosis accuracy. In this paper, a multimodal feature fusion-based deep learning method for accurate and real-time online diagnosis of rotating machinery is proposed. The proposed method can directly extract the potential frequency of abnormal features involved in the time domain data. Firstly, multimodal features corresponding to the original data, the slope data, and the curvature data are firstly extracted by three separate deep neural networks. Then, a multimodal feature fusion is developed to obtain a new fused feature that can characterize the potential frequency feature involved in the time domain data. Lastly, the fused new feature is used as the input of the Softmax classifier to achieve a real-time online diagnosis result from the frequency-type fault data. A simulation experiment and a case study of the bearing fault diagnosis confirm the high efficiency of the method proposed in this paper.

Download Full-text

Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽

10.3390/s21227467 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7467

Author(s):

Shih-Lin Lin

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Fault Diagnosis ◽

Vibration Signal ◽

Image Feature ◽

Signal Acquisition ◽

Time Varying ◽

Intelligent Fault Diagnosis ◽

Image Block ◽

Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

Download Full-text

A Physics-Based Deep Learning Approach for Fault Diagnosis of Rotating Machinery

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society ◽

10.1109/iecon.2018.8591073 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mohammadkazem Sadoughi ◽

Chao Hu

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Learning Approach

Download Full-text

A novel unsupervised deep learning network for intelligent fault diagnosis of rotating machinery

Structural Health Monitoring ◽

10.1177/1475921719897317 ◽

2020 ◽

Vol 19 (6) ◽

pp. 1745-1763 ◽

Cited By ~ 8

Author(s):

Xiaoli Zhao ◽

Minping Jia

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Affinity Propagation ◽

Intelligent Fault Diagnosis ◽

Learning Network ◽

Feature Extractor ◽

Unsupervised Deep Learning ◽

Diagnosis Method ◽

Deep Learning Network

Generally, the health conditions of rotating machinery are complicated and changeable. Meanwhile, its fault labeled information is mostly unknown. Therefore, it is man-sized to automatically capture the useful fault labeled information from the monitoring raw vibration signals. That is to say, the intelligent unsupervised learning approach has a significant influence on fault diagnosis of rotating machinery. In this study, a span-new unsupervised deep learning network can be constructed based on the proposed feature extractor (L12 sparse filtering (L12SF)) and the designed clustering extractor (Weighted Euclidean Affinity Propagation) for resolving the issue that the acquisition of fault sample labeled information is burdensome, yet costly. Naturally, the novel intelligent fault diagnosis method of rotating machinery based on unsupervised deep learning network is first presented in this study. Thereinto, the proposed unsupervised deep learning network consists of two layers of unsupervised feature extractor (L12SF) and one layer of unsupervised clustering (Weighted Euclidean Affinity Propagation). L12SF can improve the regularization performance of sparse filtering, and Weighted Euclidean Affinity Propagation can get rid of the traditional Euclidean distance in affinity propagation that cannot highlight the contribution of different features in fault clustering. To make a long story short, the frequency spectrum signals are first entered into the constructed unsupervised deep learning network for fault feature representation; afterward, the unsupervised feature learning and unsupervised fault classification of rotating machinery can be implemented. The superiority of the proposed algorithms and method is validated by two cases of rolling bearing fault dataset. Ultimately, the proposed unsupervised fault diagnosis method can provide a theoretical basis for the development of intelligent unsupervised fault diagnosis technology for rotating machinery.

Download Full-text

Self-adaptively Time-varying Filter Based Order Tracking Method and Its Application in Gearbox Fault Diagnosis

Journal of Mechanical Engineering ◽

10.3901/jme.2012.07.077 ◽

2012 ◽

Vol 48 (07) ◽

pp. 77 ◽

Cited By ~ 2

Author(s):

Fuqiang PENG

Keyword(s):

Fault Diagnosis ◽

Time Varying ◽

Tracking Method ◽

Order Tracking

Download Full-text

Research on fault diagnosis method of rotating machinery based on deep learning

2017 Prognostics and System Health Management Conference (PHM-Harbin) ◽

10.1109/phm.2017.8079279 ◽

2017 ◽

Cited By ~ 4

Author(s):

Zhouliang Chen ◽

Zhinong Li

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Diagnosis Method

Download Full-text

Research Progress of Rotating Machinery Fault Diagnosis Based on Deep Learning

E3S Web of Conferences ◽

10.1051/e3sconf/202125702030 ◽

2021 ◽

Vol 257 ◽

pp. 02030

Author(s):

Zhehua Du ◽

Xin Lin

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

High Speed ◽

Large Scale ◽

Rolling Bearing ◽

Rotating Machinery ◽

Research Progress ◽

Engineering Practice ◽

Advantages And Disadvantages ◽

Deep Learning Model

In modern production, the precision and the importance of rotating machinery is higher and higher in the direction of large-scale, high speed and automation development, so that the traditional fault diagnosis methods are insufficient to deal with massive, multi-source and high-dimensional data, cannot meet the requirements of security and reliability. Therefore, several typical deep learning models are briefly introduced at first and the application of deep learning in fault diagnosis of rotor system, gear box and rolling bearing in recent years is studied and analyzed based on its strong feature extraction ability and advantages of clustering analysis. Finally, the advantages and disadvantages of deep learning model are summarized and the fault diagnosis methods of rotating machinery are summarized and prospected based on engineering practice.

Download Full-text

Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

Shock and Vibration ◽

10.1155/2021/3083190 ◽

2021 ◽

Vol 2021 ◽

pp. 1-30

Author(s):

Wei Cui ◽

Guoying Meng ◽

Aiming Wang ◽

Xinge Zhang ◽

Jun Ding

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Fault Diagnosis ◽

Rotating Machinery ◽

Normal Operation ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Safe Production ◽

Continuous Progress ◽

Machinery Fault Diagnosis

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

Download Full-text

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Sensors ◽

10.3390/s21196614 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6614

Author(s):

Yangde Gao ◽

Cheol Hong Kim ◽

Jong-Myon Kim

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Short Term Memory ◽

Rotating Machinery ◽

Identification Accuracy ◽

Learning Method ◽

Short Term ◽

Deep Convolutional Neural Networks ◽

Term Memory ◽

Long Short Term Memory

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

Download Full-text