scholarly journals Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3949 ◽  
Author(s):  
Francisco Arellano-Espitia ◽  
Miguel Delgado-Prieto ◽  
Victor Martinez-Viol ◽  
Juan Jose Saucedo-Dorantes ◽  
Roque Alfredo Osornio-Rios
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Operating Conditions ◽  
Smart Manufacturing ◽  
Learning Technology ◽  
Electromechanical Systems ◽  
Monitoring Strategies ◽  
Complex Electromechanical Systems

Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.

scholarly journals A general end-to-end diagnosis framework for manufacturing systems

2019 ◽  
Vol 7 (2) ◽  
pp. 418-429 ◽  
Author(s):  
Ye Yuan ◽  
Guijun Ma ◽  
Cheng Cheng ◽  
Beitong Zhou ◽  
Huan Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Manufacturing Systems ◽  
Time Course ◽  
Manufacturing Sector ◽  
Smart Manufacturing ◽  
Data Sets ◽  
Learning Techniques ◽  
General Data ◽  
End To End ◽  
Manufacturing Applications

Abstract The manufacturing sector is envisioned to be heavily influenced by artificial-intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in the manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here, we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep-learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on 10 representative data sets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical cornerstone in smart manufacturing.

scholarly journals Design of Fault Diagnosis Algorithm for Electromechanical System Based on Artificial Intelligence

2021 ◽  
Vol 2050 (1) ◽  
pp. 012011
Author(s):  
Fuyou Zhao ◽  
Mingying Huo ◽  
Naiming Qi ◽  
Lianfeng Li ◽  
Weiwei Cui
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Data Sets ◽  
Learning Technology ◽  
Electrical System ◽  
Fault Recognition ◽  
Fault Features ◽  
Diagnosis Method

Abstract A relatively perfect system for the fault diagnosis of mechanical and electrical products has been formed through decades of development. Nevertheless, the traditional fault diagnosis methods fail to cope with the gradual huge mechanical and electrical system. As a result, the advantages of fault diagnosis mode driven by data are increasingly prominent. Meanwhile, the effect of fault diagnosis has exceeded the traditional fault diagnosis methods in many fields. Through the use of the deep learning technology based on artificial intelligence, it carries out mapping and fitting. By fully taking advantages of neural network, it can effectively obtain the accurate classification of fault data. A fault diagnosis method based on the fault data of mechanical and electrical system is designed in this thesis. When it comes to the basic process, it is to take data sets for different mechanical and electrical products. Through the use of feature engineering method, it extracts the fault features of data. Through the use of deep learning technology, it carries out the intelligent diagnosis. According to the experimental results, it indicates that the fault diagnosis method based on deep learning technology can distinguish a variety of fault modes in mechanical and electrical system in an effective way. What’s more, good classification results in fault recognition have been achieved by a variety of deep convolutional neural network structures, so the feasibility of the method is further verified.

A Local Steady State Approach for Fault Diagnosis of Continuous Production Systems

2006 ◽  
Author(s):  
Andrea Schrems ◽  
Hajrudin Efendic ◽  
Luigi del Re
Keyword(s):  
Steady State ◽  
Fault Diagnosis ◽  
Production Systems ◽  
Continuous Production ◽  
Measurement Data ◽  
Original System ◽  
Relevant Information ◽  
Industrial Applications ◽  
Operating Conditions ◽  
Sufficient Information

Data based models are frequently and successfully used to monitor the operation state and detect faults in industrial plants. Deriving these models from data, however, can be quite difficult and unreliable, especially if data from actual operation have to be used, as the resulting optimization problem often becomes ill conditioned: available data may not contain sufficient information, in particular for continuous production systems which are often run for longer times under almost constant operating conditions. This paper presents a local steady state approach for such problems based on complexity reduction: data are pre-processed prior to the modeling phase to allow the build-up of reduced complexity models, thus replacing the original ill conditioned problem with a better conditioned one. A method is presented to extract the relevant information about the process from measurement data in such a way to guarantee that the resulting simplified models will retain the essential characteristics of the original system required to perform fault diagnosis successfully. This approach has been used in different industrial applications and has proved reliable and efficient.

scholarly journals Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2497
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Cristian Uță ◽  
Roxana Ștefănescu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Manufacturing Systems ◽  
Process Management ◽  
Production Systems ◽  
Big Data Analytics ◽  
Eligibility Criteria ◽  
Cyber Physical Production Systems

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

scholarly journals Adaptive Transfer Learning Based on a Two-Stream Densely Connected Residual Shrinkage Network for Transformer Fault Diagnosis over Vibration Signals

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2130
Author(s):  
Xiaoyan Liu ◽  
Yigang He ◽  
Lei Wang
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Data Set ◽  
Vibration Signals ◽  
Time Frequency ◽  
Diagnosis Method ◽  
Transformer Fault

Vibration signal analysis is an efficient online transformer fault diagnosis method for improving the stability and safety of power systems. Operation in harsh interference environments and the lack of fault samples are the most challenging aspects of transformer fault diagnosis. High-precision performance is difficult to achieve when using conventional fault diagnosis methods. Thus, this study proposes a transformer fault diagnosis method based on the adaptive transfer learning of a two-stream densely connected residual shrinkage network over vibration signals. First, novel time-frequency analysis methods (i.e., Synchrosqueezed Wavelet Transform and Synchrosqueezed Generalized S-transform) are proposed to convert vibration signals into different images, effectively expanding the samples and extracting effective features of signals. Second, a Two-stream Densely Connected Residual Shrinkage (TSDen2NetRS) network is presented to achieve a high accuracy fault diagnosis under different working conditions. Furthermore, the Residual Shrinkage layer (RS layer) is applied as a nonlinear transformation layer to the deep learning framework to remove unimportant features and enhance anti-interference performance. Lastly, an adaptive transfer learning algorithm that can automatically select the source data set by using the domain measurement method is proposed. This algorithm accelerates the training of the deep learning network and improves accuracy when the number of samples is small. Vibration experiments of transformers are conducted under different operating conditions, and their results show the effectiveness and robustness of the proposed method.

scholarly journals Bearing Fault Diagnosis Based on Multiscale Convolutional Neural Network Using Data Augmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Seungmin Han ◽  
Seokju Oh ◽  
Jongpil Jeong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Feature Learning ◽  
Learning Technology ◽  
Financial Loss ◽  
Training Time ◽  
Imbalance Problem

Bearings are one of the most important parts of a rotating machine. Bearing failure can lead to mechanical failure, financial loss, and even personal injury. In recent years, various deep learning techniques have been used to diagnose bearing faults in rotating machines. However, deep learning technology has a data imbalance problem because it requires huge amounts of data. To solve this problem, we used data augmentation techniques. In addition, Convolutional Neural Network, one of the deep learning models, is a method capable of performing feature learning without prior knowledge. However, since conventional fault diagnosis based on CNN can only extract single-scale features, not only useful information may be lost but also domain shift problems may occur. In this paper, we proposed a Multiscale Convolutional Neural Network (MSCNN) to extract more powerful and differentiated features from raw signals. MSCNN can learn more powerful feature expression than conventional CNN through multiscale convolution operation and reduce the number of parameters and training time. The proposed model proved better results and validated the effectiveness of the model compared to 2D-CNN and 1D-CNN.

scholarly journals Software design of rotating machinery fault diagnosis system based on deep learning

2021 ◽  
Vol 260 ◽  
pp. 03006
Author(s):  
Xiaofeng He ◽  
Xiaofeng Liu ◽  
Xiulian Lu ◽  
Lipeng He ◽  
Yunxiang Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Operating Conditions ◽  
Diagnosis System ◽  
Training Requirements ◽  
Learning Framework ◽  
Software Interface ◽  
Python Language ◽  
Fault Diagnosis System

With the development of Industry 4.0, in order to meet the needs of intelligent fault diagnosis of rotating machinery in the industrial field, this paper developed a fault diagnosis system for rotating machinery based on deep learning and wavelet transform methods. The system is based on the Python language and mainly combines the PyQt graphical interface framework and the TensorFlow machine learning framework to complete the training requirements for historical or online fault data, and perform online monitoring and diagnosis of equipment operating conditions. The diagnostic accuracy of the system test results is more than 95%, the software interface is friendly, the algorithm generalization ability is good, and the reliability is strong. It provides guidance for the diagnosis of rotating machinery.

Deep learning for fault diagnosis and prognosis in manufacturing systems

2019 ◽  
Vol 110 ◽  
pp. 1-2 ◽  
Author(s):  
Ruqiang Yan ◽  
Xuefeng Chen ◽  
Peng Wang ◽  
Darian M. Onchis
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Manufacturing Systems ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis
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