179 research outputs found
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
The demand of artificial intelligent adoption for condition-based maintenance
strategy is astonishingly increased over the past few years. Intelligent fault
diagnosis is one critical topic of maintenance solution for mechanical systems.
Deep learning models, such as convolutional neural networks (CNNs), have been
successfully applied to fault diagnosis tasks for mechanical systems and
achieved promising results. However, for diverse working conditions in the
industry, deep learning suffers two difficulties: one is that the well-defined
(source domain) and new (target domain) datasets are with different feature
distributions; another one is the fact that insufficient or no labelled data in
target domain significantly reduce the accuracy of fault diagnosis. As a novel
idea, deep transfer learning (DTL) is created to perform learning in the target
domain by leveraging information from the relevant source domain. Inspired by
Wasserstein distance of optimal transport, in this paper, we propose a novel
DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based
Deep Transfer Learning (WD-DTL), to learn domain feature representations
(generated by a CNN based feature extractor) and to minimize the distributions
between the source and target domains through adversarial training. The
effectiveness of the proposed WD-DTL is verified through 3 transfer scenarios
and 16 transfer fault diagnosis experiments of both unsupervised and supervised
(with insufficient labelled data) learning. We also provide a comprehensive
analysis of the network visualization of those transfer tasks
A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions
In recent years, intelligent fault diagnosis technology with the deep learning algorithm has been widely used in the manufacturing industry for substituting time-consuming human analysis method to enhance the efficiency of fault diagnosis. The rolling bearing as the connection between the rotor and support is the crucial component in rotating equipment. However, the working condition of the rolling bearing is under changing with complex operation demand, which will significantly degrade the performance of the intelligent fault diagnosis method. In this paper, a new deep transfer model based on Wasserstein distance guided multi-adversarial networks (WDMAN) is proposed to address this problem. The WDMAN model exploits complex feature space structures to enable the transfer of different data distributions based on multiple domain critic networks. The essence of our method is learning the shared feature representation by minimizing the Wasserstein distance between the source domain and target domain distribution in an adversarial training way. The experiment results demonstrate that our model outperforms the state-of-the-art methods on rolling bearing fault diagnosis under different working conditions. The t-distributed stochastic neighbor embedding (t-SNE) technology is used to visualize the learned domain invariant feature and investigate the transferability behind the great performance of our proposed model
Exploiting gan as an oversampling method for imbalanced data augmentation with application to the fault diagnosis of an industrial robot
O diagnóstico inteligente de falhas baseado em aprendizagem máquina geralmente requer
um conjunto de dados balanceados para produzir um desempenho aceitável. No
entanto, a obtenção de dados quando o equipamento industrial funciona com falhas é
uma tarefa desafiante, resultando frequentemente num desequilíbrio entre dados obtidos
em condições nominais e com falhas. As técnicas de aumento de dados são das
abordagens mais promissoras para mitigar este problema.
Redes adversárias generativas (GAN) são um tipo de modelo generativo que consiste
de um módulo gerador e de um discriminador. Por meio de aprendizagem adversária
entre estes módulos, o gerador otimizado pode produzir padrões sintéticos que
podem ser usados para amumento de dados.
Investigamos se asGANpodem ser usadas como uma ferramenta de sobre amostra-
-gem para compensar um conjunto de dados desequilibrado em uma tarefa de diagnóstico
de falhas num manipulador robótico industrial. Realizaram-se uma série de
experiências para validar a viabilidade desta abordagem. A abordagem é comparada
com seis cenários, incluindo o método clássico de sobre amostragem SMOTE. Os resultados
mostram que a GAN supera todos os cenários comparados.
Para mitigar dois problemas reconhecidos no treino das GAN, ou seja, instabilidade
de treino e colapso de modo, é proposto o seguinte.
Propomos uma generalização da GAN de erro quadrado médio (MSE GAN) da
Wasserstein GAN com penalidade de gradiente (WGAN-GP), referida como VGAN (GAN baseado numa matriz V) para mitigar a instabilidade de treino. Além disso,
propomos um novo critério para rastrear o modelo mais adequado durante o treino.
Experiências com o MNIST e no conjunto de dados do manipulador robótico industrial
mostram que o VGAN proposto supera outros modelos competitivos.
A rede adversária generativa com consistência de ciclo (CycleGAN) visa lidar com
o colapso de modo, uma condição em que o gerador produz pouca ou nenhuma variabilidade.
Investigamos a distância fatiada de Wasserstein (SWD) na CycleGAN. O
SWD é avaliado tanto no CycleGAN incondicional quanto no CycleGAN condicional
com e sem mecanismos de compressão e excitação. Mais uma vez, dois conjuntos de
dados são avaliados, ou seja, o MNIST e o conjunto de dados do manipulador robótico
industrial. Os resultados mostram que o SWD tem menor custo computacional e supera
o CycleGAN convencional.Machine learning based intelligent fault diagnosis often requires a balanced data set for
yielding an acceptable performance. However, obtaining faulty data from industrial
equipment is challenging, often resulting in an imbalance between data acquired in
normal conditions and data acquired in the presence of faults. Data augmentation
techniques are among the most promising approaches to mitigate such issue.
Generative adversarial networks (GAN) are a type of generative model consisting
of a generator module and a discriminator. Through adversarial learning between
these modules, the optimised generator can produce synthetic patterns that can be
used for data augmentation.
We investigate whether GAN can be used as an oversampling tool to compensate
for an imbalanced data set in an industrial robot fault diagnosis task. A series of experiments
are performed to validate the feasibility of this approach. The approach is
compared with six scenarios, including the classical oversampling method (SMOTE).
Results show that GAN outperforms all the compared scenarios.
To mitigate two recognised issues in GAN training, i.e., instability and mode collapse,
the following is proposed.
We proposed a generalization of both mean sqaure error (MSE GAN) and Wasserstein
GAN with gradient penalty (WGAN-GP), referred to as VGAN (the V-matrix
based GAN) to mitigate training instability. Also, a novel criterion is proposed to keep
track of the most suitable model during training. Experiments on both the MNIST and the industrial robot data set show that the proposed VGAN outperforms other
competitive models.
Cycle consistency generative adversarial network (CycleGAN) is aiming at dealing
with mode collapse, a condition where the generator yields little to none variability.
We investigate the sliced Wasserstein distance (SWD) for CycleGAN. SWD is evaluated
in both the unconditional CycleGAN and the conditional CycleGAN with and
without squeeze-and-excitation mechanisms. Again, two data sets are evaluated, i.e.,
the MNIST and the industrial robot data set. Results show that SWD has less computational
cost and outperforms conventional CycleGAN
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
Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data
CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs
Federated Domain Generalization: A Survey
Machine learning typically relies on the assumption that training and testing
distributions are identical and that data is centrally stored for training and
testing. However, in real-world scenarios, distributions may differ
significantly and data is often distributed across different devices,
organizations, or edge nodes. Consequently, it is imperative to develop models
that can effectively generalize to unseen distributions where data is
distributed across different domains. In response to this challenge, there has
been a surge of interest in federated domain generalization (FDG) in recent
years. FDG combines the strengths of federated learning (FL) and domain
generalization (DG) techniques to enable multiple source domains to
collaboratively learn a model capable of directly generalizing to unseen
domains while preserving data privacy. However, generalizing the federated
model under domain shifts is a technically challenging problem that has
received scant attention in the research area so far. This paper presents the
first survey of recent advances in this area. Initially, we discuss the
development process from traditional machine learning to domain adaptation and
domain generalization, leading to FDG as well as provide the corresponding
formal definition. Then, we categorize recent methodologies into four classes:
federated domain alignment, data manipulation, learning strategies, and
aggregation optimization, and present suitable algorithms in detail for each
category. Next, we introduce commonly used datasets, applications, evaluations,
and benchmarks. Finally, we conclude this survey by providing some potential
research topics for the future
An improved CTGAN for data processing method of imbalanced disk failure
To address the problem of insufficient failure data generated by disks and
the imbalance between the number of normal and failure data. The existing
Conditional Tabular Generative Adversarial Networks (CTGAN) deep learning
methods have been proven to be effective in solving imbalance disk failure
data. But CTGAN cannot learn the internal information of disk failure data very
well. In this paper, a fault diagnosis method based on improved CTGAN, a
classifier for specific category discrimination is added and a discriminator
generate adversarial network based on residual network is proposed. We named it
Residual Conditional Tabular Generative Adversarial Networks (RCTGAN). Firstly,
to enhance the stability of system a residual network is utilized. RCTGAN uses
a small amount of real failure data to synthesize fake fault data; Then, the
synthesized data is mixed with the real data to balance the amount of normal
and failure data; Finally, four classifier (multilayer perceptron, support
vector machine, decision tree, random forest) models are trained using the
balanced data set, and the performance of the models is evaluated using G-mean.
The experimental results show that the data synthesized by the RCTGAN can
further improve the fault diagnosis accuracy of the classifier
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Effective Prognostics and Health Management (PHM) relies on accurate
prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction
techniques rely heavily on the representativeness of the available
time-to-failure trajectories. Therefore, these methods may not perform well
when applied to data from new units of a fleet that follow different operating
conditions than those they were trained on. This is also known as domain
shifts. Domain adaptation (DA) methods aim to address the domain shift problem
by extracting domain invariant features. However, DA methods do not distinguish
between the different phases of operation, such as steady states or transient
phases. This can result in misalignment due to under- or over-representation of
different operation phases. This paper proposes two novel DA approaches for RUL
prediction based on an adversarial domain adaptation framework that considers
the different phases of the operation profiles separately. The proposed
methodologies align the marginal distributions of each phase of the operation
profile in the source domain with its counterpart in the target domain. The
effectiveness of the proposed methods is evaluated using the New Commercial
Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan
engines operating in one of the three different flight classes (short, medium,
and long) are treated as separate domains. The experimental results show that
the proposed methods improve the accuracy of RUL predictions compared to
current state-of-the-art DA methods.Comment: 18 pages,11 figure
Multi-level semantic information guided image generation for few-shot steel surface defect classification
Surface defect classification is one of key points in the field of steel manufacturing. It remains challenging primarily due to the rare occurrence of defect samples and the similarity between different defects. In this paper, a multi-level semantic method based on residual adversarial learning with Wasserstein divergence is proposed to realize sample augmentation and automatic classification of various defects simultaneously. Firstly, the residual module is introduced into model structure of adversarial learning to optimize the network structure and effectively improve the quality of samples generated by model. By substituting original classification layer with multiple convolution layers in the network framework, the feature extraction capability of model is further strengthened, enhancing the classification performance of model. Secondly, in order to better capture different semantic information, we design a multi-level semantic extractor to extract rich and diverse semantic features from real-world images to efficiently guide sample generation. In addition, the Wasserstein divergence is introduced into the loss function to effectively solve the problem of unstable network training. Finally, high-quality defect samples can be generated through adversarial learning, effectively expanding the limited training samples for defect classification. The experimental results substantiate that our proposed method can not only generate high-quality defect samples, but also accurately achieve the classification of defect detection samples
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