383 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
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
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
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
Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM
A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance
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
Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis
Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an
effective and flexible solution, attracting extensive research. Deep neural
networks can learn rich representations from vast amounts of representative
labeled data for various applications. In IFD, they achieve high classification
performance from signals in an end-to-end manner, without requiring extensive
domain knowledge. However, deep learning models usually only perform well on
the data distribution they have been trained on. When applied to a different
distribution, they may experience performance drops. This is also observed in
IFD, where assets are often operated in working conditions different from those
in which labeled data have been collected. Unsupervised domain adaptation (UDA)
deals with the scenario where labeled data are available in a source domain,
and only unlabeled data are available in a target domain, where domains may
correspond to operating conditions. Recent methods rely on training with
confident pseudo-labels for target samples. However, the confidence-based
selection of pseudo-labels is hindered by poorly calibrated confidence
estimates in the target domain, primarily due to over-confident predictions,
which limits the quality of pseudo-labels and leads to error accumulation. In
this paper, we propose a novel UDA method called Calibrated Adaptive Teacher
(CAT), where we propose to calibrate the predictions of the teacher network
throughout the self-training process, leveraging post-hoc calibration
techniques. We evaluate CAT on domain-adaptive IFD and perform extensive
experiments on the Paderborn benchmark for bearing fault diagnosis under
varying operating conditions. Our proposed method achieves state-of-the-art
performance on most transfer tasks.Comment: 23 pages. Under revie
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