8,309 research outputs found
Fault Diagnosis of Transfer Learning Equipment Based on Cloud Edge Collaboration + Confrontation Network
With the continuous improvement of product quality, production efficiency, and complexity, higher requirements are put forward for the reliability and stability of equipment, and the difficulty of real-time diagnosis of faults and functional failures is also increasing. The traditional fault diagnosis methods based on signal processing and Convolutional neural network cannot meet the requirements of on-site online real-time fault diagnosis of equipment. One is that the vibration signals on the industrial site are superimposed on each other, nonlinear and unstable and traditional feature extraction methods take a long time, resulting in unstable extraction results. Second, massive data and fault diagnosis algorithms need rich computing and storage resources. The traditional Convolutional neural network method conflicts with the real-time response requirements of fault diagnosis. At the same time, different models of fault diagnosis models have poor generalization ability, and the diagnostic accuracy is not high or even impossible to diagnose. To solve the above problems, this paper proposes a fault diagnosis method based on industrial Internet platform, which is equipment cloud edge collaboration + adaptive countermeasure network Transfer learning. On the edge side, the vibration signals collected from key components of the model are processed using empirical mode decomposition (EEMD) to solve the problem of signal nonlinearity and stationarity. In the cloud, EEMD signals of different models are decomposed into source domain and target domain for confrontation training, which is used as the input of the improved domain adversarial network model DANN (Domain Adversarial Neural Networks), so as to improve the accuracy of fault diagnosis of different models by using cloud computing power and the improved adversarial network Transfer learning algorithm. Through the analysis of experimental data, this paper verifies that the model after the confrontation network Transfer learning is more accurate than the traditional fault diagnosis method. Through the coordination of computing resources and real-time requirements, real-time diagnosis of cloud side collaborative bearing fault is realized
Information Theory and Its Application in Machine Condition Monitoring
Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries
Deep adversarial domain adaptation model for bearing fault diagnosis
Fault diagnosis of rolling bearings is an essential process for improving the reliability and safety of the rotating machinery. It is always a major challenge to ensure fault diagnosis accuracy in particular under severe working conditions. In this article, a deep adversarial domain adaptation (DADA) model is proposed for rolling bearing fault diagnosis. This model constructs an adversarial adaptation network to solve the commonly encountered problem in numerous real applications: the source domain and the target domain are inconsistent in their distribution. First, a deep stack autoencoder (DSAE) is combined with representative feature learning for dimensionality reduction, and such a combination provides an unsupervised learning method to effectively acquire fault features. Meanwhile, domain adaptation and recognition classification are implemented using a Softmax classifier to augment classification accuracy. Second, the effects of the number of hidden layers in the stack autoencoder network, the number of neurons in each hidden layer, and the hyperparameters of the proposed fault diagnosis algorithm are analyzed. Third, comprehensive analysis is performed on real data to validate the performance of the proposed method; the experimental results demonstrate that the new method outperforms the existing machine learning and deep learning methods, in terms of classification accuracy and generalization ability
Fault diagnosis for electromechanical drivetrains using a joint distribution optimal deep domain adaptation approach
Robust and reliable drivetrain is important for preventing electromechanical (e.g., wind turbine) downtime. In recent years, advanced machine learning (ML) techniques including deep learning have been introduced to improve fault diagnosis performance for electromechanical systems. However, electromechanical systems (e.g., wind turbine) operate in varying working conditions, meaning that the distribution of the test data (in the target domain) is different from the training data used for model training, and the diagnosis performance of an ML method may become downgraded for practical applications. This paper proposes a joint distribution optimal deep domain adaptation approach (called JDDA) based auto-encoder deep classifier for fault diagnosis of electromechanical drivetrains under the varying working conditions. First, the representative features are extracted by the deep auto-encoder. Then, the joint distribution adaptation is used to implement the domain adaptation, so the classifier trained with the source domain features can be used to classify the target domain data. Lastly, the classification performance of the proposed JDDA is tested using two test-rig datasets, compared with three traditional machine learning methods and two domain adaptation approaches. Experimental results show that the JDDA can achieve better performance compared with the reference machine learning, deep learning and domain adaptation approaches
Smart filter aided domain adversarial neural network: An unsupervised domain adaptation method for fault diagnosis in noisy industrial scenarios
The application of unsupervised domain adaptation (UDA)-based fault diagnosis
methods has shown significant efficacy in industrial settings, facilitating the
transfer of operational experience and fault signatures between different
operating conditions, different units of a fleet or between simulated and real
data. However, in real industrial scenarios, unknown levels and types of noise
can amplify the difficulty of domain alignment, thus severely affecting the
diagnostic performance of deep learning models. To address this issue, we
propose an UDA method called Smart Filter-Aided Domain Adversarial Neural
Network (SFDANN) for fault diagnosis in noisy industrial scenarios. The
proposed methodology comprises two steps. In the first step, we develop a smart
filter that dynamically enforces similarity between the source and target
domain data in the time-frequency domain. This is achieved by combining a
learnable wavelet packet transform network (LWPT) and a traditional wavelet
packet transform module. In the second step, we input the data reconstructed by
the smart filter into a domain adversarial neural network (DANN). To learn
domain-invariant and discriminative features, the learnable modules of SFDANN
are trained in a unified manner with three objectives: time-frequency feature
proximity, domain alignment, and fault classification. We validate the
effectiveness of the proposed SFDANN method based on two fault diagnosis cases:
one involving fault diagnosis of bearings in noisy environments and another
involving fault diagnosis of slab tracks in a train-track-bridge coupling
vibration system, where the transfer task involves transferring from numerical
simulations to field measurements. Results show that compared to other
representative state of the art UDA methods, SFDANN exhibits superior
performance and remarkable stability
A multitask-aided transfer learning-based diagnostic framework for bearings under inconsistent working conditions.
Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions
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
Federated Learning with Uncertainty-Based Client Clustering for Fleet-Wide Fault Diagnosis
Operators from various industries have been pushing the adoption of wireless
sensing nodes for industrial monitoring, and such efforts have produced
sizeable condition monitoring datasets that can be used to build diagnosis
algorithms capable of warning maintenance engineers of impending failure or
identifying current system health conditions. However, single operators may not
have sufficiently large fleets of systems or component units to collect
sufficient data to develop data-driven algorithms. Collecting a satisfactory
quantity of fault patterns for safety-critical systems is particularly
difficult due to the rarity of faults. Federated learning (FL) has emerged as a
promising solution to leverage datasets from multiple operators to train a
decentralized asset fault diagnosis model while maintaining data
confidentiality. However, there are still considerable obstacles to overcome
when it comes to optimizing the federation strategy without leaking sensitive
data and addressing the issue of client dataset heterogeneity. This is
particularly prevalent in fault diagnosis applications due to the high
diversity of operating conditions and system configurations. To address these
two challenges, we propose a novel clustering-based FL algorithm where clients
are clustered for federating based on dataset similarity. To quantify dataset
similarity between clients without explicitly sharing data, each client sets
aside a local test dataset and evaluates the other clients' model prediction
accuracy and uncertainty on this test dataset. Clients are then clustered for
FL based on relative prediction accuracy and uncertainty
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
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