1 research outputs found
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Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : 곡과λν κΈ°κ³ν곡곡νλΆ, 2023. 2. μ€λ³λ.Rotating machinery is widely used in many industrial sites, including manufacturing and power generation. Unpredicted failures in these systems can result in huge economic and human losses. To prevent this situation, fault diagnosis studies have gathered much attention, with the goal of operating rotating machines without the occurrence of any unpredicted problems. Fault diagnosis methods aim to accurately detect any abnormality prior to failure and classify the health conditions of the target system. Recently, fault diagnosis studies using deep learning have achieved excellent performance thanks to the ability of new methods to autonomously extract meaningful features.
For this purpose, two types of signals of different resolutions are measured from rotating machinery, specifically: operation signals and vibration signals. Operation signals, which are measured with a low sampling rate, are obtained in real-time and contain various types of condition parameters that enable global monitoring of the system. Vibration signals with a high sampling rate are obtained when an event occurs, not in real-time. Using these signals of different resolutions, two sub-tasks of fault diagnosis β anomaly detection and fault identification β are performed. Anomaly detection, which is conducted with operation signals, is a task to detect abnormalities in a system before those abnormalities develop into a hard failure. This is considered macro-level fault diagnosis. When performing anomaly detection, the normal data is modeled by unsupervised learning, a residual is calculated, and a threshold is determined. If the residual becomes larger than the threshold, the system is regarded as an anomaly condition. Fault identification is performed to classify the health conditions of the system using vibration signals; this is viewed as micro-level fault diagnosis. For fault identification, supervised learning is used to train a deep-learning-based classifier; thus, a large amount of labeled data is required for the training. Since fault data is insufficient in real industrial fields, data augmentation is necessary to augment the fault data. Currently, a variational auto-encoder or a generative adversarial network are the approaches most widely used for data augmentation.
Anomaly detection and fault identification have been studied separately. If both tasks are integrated, macro- and micro-level fault diagnosis can be implemented. However, there are three issues that must be handled to develop a deep-learning-based methodology for macro- and micro-level fault diagnosis. First, conventional anomaly detection methods produce frequent false alarms; in other words, they may indicate a problem even if there is no anomaly in the system. This problem occurs because conventional approaches may model the normal data inadequately or set a wrong threshold; for example, one that does not consider the fluctuations in the normal data. Second, the prior generative-network-based augmentation approach has inborn limitations due to its structural properties. With this method, signals of various lengths cannot be generated because the architecture is fixed. Also, incorrect samples can be generated if the latent vectors are sampled wrongly. The final issue with health classification is that the performance of a classifier can be affected by noise in the input data. Since noise can distort the data distribution, it is difficult for a classifier to correctly classify the noisy data.
Based on the current state of the field, this doctoral dissertation proposes a deep-learning-based methodology for macro- and micro-level fault diagnosis using operation and vibration signals from rotating machinery. The first research thrust proposes new methods for modeling and threshold setting to reduce false alarms related to anomaly detection. The proposed modeling method is developed by applying ensemble and denoising techniques to auto-encoders. Further, a threshold is newly proposed using the joint distribution of the output and the residual. Consequently, the proposed method considers the fluctuations in the normal data, which can significantly reduce false alarms. The second research thrust proposes a new generative network to generate signals of variable lengths. The proposed network, whose input and output are the time and amplitude, respectively, is designed to learn the frequency information of the training data. The proposed method is implemented to reflect the signal processing knowledge, including the use of the Nyquist theorem. After the training is finished, the proposed model can produce signals of various lengths in the desired time range. The proposed approach can also focus on the characteristic frequency components, thanks to attention blocks. The third research thrust proposes a novel training method that simultaneously learns the classification and denoising tasks. In the proposed scheme, multi-task learning is used to allow a classifier to solve the classification and denoising tasks concurrently. The proposed method can be applied to any deep-learning algorithm, regardless of the network type. The classifier that is trained by the proposed method can classify the health conditions, as well as remove noise in the input signals.νμ κΈ°κ³λ μ μ‘° λ° λ°μ κ³Ό κ°μ΄ λ€μν μ°μ
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Έμ΄μ¦λ μ κ±°ν μ μλ€.Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Research Scope and Overview 5
1.3 Dissertation Layout 9
Chapter 2 Technical Background and Literature Review 10
2.1 Fault Diagnosis Methods of Rotating Machinery 10
2.2 Low- and High-resolution Signals from Rotating Machinery 13
2.3 Review of Deep Learning Algorithms 15
2.3.1 One-dimensional Convolutional Neural Network (1D CNN) 16
2.3.2 Long Short-term Memory (LSTM) 17
2.4 Deep-learning-based Macro- and Micro-level Fault Diagnosis Methods 19
2.4.1 Anomaly Detection 23
2.4.2 Data Augmentation 28
2.4.3 Health Classification 32
2.5 Summary and Discussion 35
Chapter 3 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) for Anomaly Detection 37
3.1 Background: Deep-learning-based Anomaly Detection 39
3.1.1 Conventional Methods to Model the Normal Data 39
3.1.2 Conventional Methods to Set a Threshold 41
3.2 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) 42
3.3 Performance Evaluation Metrics 47
3.4 Description of the Validation Datasets 50
3.5 Validation of the Proposed Method 58
3.5.1 Case Study 1: Dataset A1 58
3.5.2 Case Study 2: Dataset A2 74
3.5.3 Analysis and Discussion 89
3.6 Summary and Discussion 95
Chapter 4 Frequency-learning Generative Network (FLGN) for Data Augmentation 96
4.1 Background: Fourier Series 97
4.2 Frequency-learning Generative Network (FLGN) 99
4.2.1 Problem Formulation 99
4.2.2 Overall Procedure of FLGN 100
4.2.3 Deep-learning Implementation Details to Reflect Signals Processing Knowledge 105
4.3 Experimental Implementation Setting 106
4.3.1 Hyper-parameter Setting 107
4.3.2 Evaluation Scheme 107
4.4 Description of the Validation Datasets 111
4.5 Validation of the Proposed Method 119
4.5.1 Case Study 1: Simulated Signal 119
4.5.2 Case Study 2: RK4 Testbed Dataset 128
4.5.3 Case Study 3: MAFAULDA 141
4.5.4 Analysis and Discussion 153
4.6 Summary and Discussion 158
Chapter 5 Multi-task Learning of Classification and Denoising (MLCD) for Health Classification 159
5.1 Background: Multi-task Learning 160
5.2 Multi-task Learning of Classification and Denoising (MLCD) 161
5.2.1 Overall Procedure of MLCD 162
5.2.2 Integration with LSTM: MLCD-LSTM 165
5.2.3 Integration with 1D CNN: MLCD-1D CNN 166
5.3 Preprocessing Techniques 170
5.4 Description of the Validation Datasets 172
5.5 Validation of the Proposed Method 176
5.5.1 Case Study 1: MLCD-LSTM 176
5.5.2 Case Study 2: MLCD-1D CNN 183
5.6 Summary and Discussion 190
Chapter 6 Conclusion 191
6.1 Contributions and Significance 191
6.2 Suggestions for Future Research 194
References 196
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