12,192 research outputs found
Domain Adaptive Transfer Learning for Fault Diagnosis
Thanks to digitization of industrial assets in fleets, the ambitious goal of
transferring fault diagnosis models fromone machine to the other has raised
great interest. Solving these domain adaptive transfer learning tasks has the
potential to save large efforts on manually labeling data and modifying models
for new machines in the same fleet. Although data-driven methods have shown
great potential in fault diagnosis applications, their ability to generalize on
new machines and new working conditions are limited because of their tendency
to overfit to the training set in reality. One promising solution to this
problem is to use domain adaptation techniques. It aims to improve model
performance on the target new machine. Inspired by its successful
implementation in computer vision, we introduced Domain-Adversarial Neural
Networks (DANN) to our context, along with two other popular methods existing
in previous fault diagnosis research. We then carefully justify the
applicability of these methods in realistic fault diagnosis settings, and offer
a unified experimental protocol for a fair comparison between domain adaptation
methods for fault diagnosis problems.Comment: Presented at 2019 Prognostics and System Health Management Conference
(PHM 2019) in Paris, Franc
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
Constructive Incremental Learning for Fault Diagnosis of Rolling Bearings with Ensemble Domain Adaptation
Given the prevalence of rolling bearing fault diagnosis as a practical issue
across various working conditions, the limited availability of samples
compounds the challenge. Additionally, the complexity of the external
environment and the structure of rolling bearings often manifests faults
characterized by randomness and fuzziness, hindering the effective extraction
of fault characteristics and restricting the accuracy of fault diagnosis. To
overcome these problems, this paper presents a novel approach termed
constructive Incremental learning-based ensemble domain adaptation (CIL-EDA)
approach. Specifically, it is implemented on stochastic configuration networks
(SCN) to constructively improve its adaptive performance in multi-domains.
Concretely, a cloud feature extraction method is employed in conjunction with
wavelet packet decomposition (WPD) to capture the uncertainty of fault
information from multiple resolution aspects. Subsequently, constructive
Incremental learning-based domain adaptation (CIL-DA) is firstly developed to
enhance the cross-domain learning capability of each hidden node through domain
matching and construct a robust fault classifier by leveraging limited labeled
data from both target and source domains. Finally, fault diagnosis results are
obtained by a majority voting of CIL-EDA which integrates CIL-DA and parallel
ensemble learning. Experimental results demonstrate that our CIL-DA outperforms
several domain adaptation methods and CIL-EDA consistently outperforms
state-of-art fault diagnosis methods in few-shot scenarios
Zero-Shot Motor Health Monitoring by Blind Domain Transition
Continuous long-term monitoring of motor health is crucial for the early
detection of abnormalities such as bearing faults (up to 51% of motor failures
are attributed to bearing faults). Despite numerous methodologies proposed for
bearing fault detection, most of them require normal (healthy) and abnormal
(faulty) data for training. Even with the recent deep learning (DL)
methodologies trained on the labeled data from the same machine, the
classification accuracy significantly deteriorates when one or few conditions
are altered. Furthermore, their performance suffers significantly or may
entirely fail when they are tested on another machine with entirely different
healthy and faulty signal patterns. To address this need, in this pilot study,
we propose a zero-shot bearing fault detection method that can detect any fault
on a new (target) machine regardless of the working conditions, sensor
parameters, or fault characteristics. To accomplish this objective, a 1D
Operational Generative Adversarial Network (Op-GAN) first characterizes the
transition between normal and fault vibration signals of (a) source machine(s)
under various conditions, sensor parameters, and fault types. Then for a target
machine, the potential faulty signals can be generated, and over its actual
healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN
fault detector can then be trained to detect the real faulty condition in real
time whenever it occurs. To validate the proposed approach, a new benchmark
dataset is created using two different motors working under different
conditions and sensor locations. Experimental results demonstrate that this
novel approach can accurately detect any bearing fault achieving an average
recall rate of around 89% and 95% on two target machines regardless of its
type, severity, and location.Comment: 13 pages, 9 figures, Journa
Vibration-based adaptive novelty detection method for monitoring faults in a kinematic chain
Postprint (published version
A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults
Real-time acquisition of large amounts of machine operating data is now increasingly common due to recent advances in Industry 4.0 technologies. A key benefit to factory operators of this large scale data acquisition is in the ability to perform real-time condition monitoring and early-stage fault detection and diagnosis on industrial machinery—with the potential to reduce machine down-time and thus operating costs. The main contribution of this work is the development of an intelligent fault diagnosis method capable of operating on these real-time data streams to provide early detection of developing problems under variable operating conditions. We propose a novel dual-path recurrent neural network with a wide first kernel and deep convolutional neural network pathway (RNN-WDCNN) capable of operating on raw temporal signals such as vibration data to diagnose rolling element bearing faults in data acquired from electromechanical drive systems. RNN-WDCNN combines elements of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture distant dependencies in time series data and suppress high-frequency noise in the input signals. Experimental results on the benchmark Case Western Reserve University (CWRU) bearing fault dataset show RNN-WDCNN outperforms current state-of-the-art methods in both domain adaptation and noise rejection tasks
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
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