20,222 research outputs found
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
Combining model-based diagnosis and data-driven anomaly classifiers for fault isolation
Machine learning can be used to automatically process sensor data and create data-driven models for prediction and classification. However, in applications such as fault diagnosis, faults are rare events and learning models for fault classification is complicated because of lack of relevant training data. This paper proposes a hybrid diagnosis system design which combines model-based residuals with incremental anomaly classifiers. The proposed method is able to identify unknown faults and also classify multiple-faults using only single-fault training data. The proposed method is verified using a physical model and data collected from an internal combustion engine.Funding agencies: Volvo Car Corporation in Gothenburg, Sweden</p
Evolving class for SVM's incremental learning.
International audienceThe good generalization performance of support vector machines (SVM) has made them a popular tool in artificial intelligence community. In this paper, we prove that SVM multi class algorithms are not equivalent for all classification problems we present a new approach for incremental learning using SVM that create a rejection class which would be interesting for fault diagnosis where fault classes usually evolve with time : It is when some new samples may be rejected by all the current classes. Hence, these samples may correspond to a new fault (a new class) which may appear after the first training step
Fault diagnosis for IP-based network with real-time conditions
BACKGROUND:
Fault diagnosis techniques have been based on many paradigms, which derive from diverse areas
and have different purposes: obtaining a representation model of the network for fault localization,
selecting optimal probe sets for monitoring network devices, reducing fault detection time, and
detecting faulty components in the network. Although there are several solutions for diagnosing
network faults, there are still challenges to be faced: a fault diagnosis solution needs to always be
available and able enough to process data timely, because stale results inhibit the quality and speed
of informed decision-making. Also, there is no non-invasive technique to continuously diagnose the
network symptoms without leaving the system vulnerable to any failures, nor a resilient technique
to the network's dynamic changes, which can cause new failures with different symptoms.
AIMS:
This thesis aims to propose a model for the continuous and timely diagnosis of IP-based networks
faults, independent of the network structure, and based on data analytics techniques.
METHOD(S):
This research's point of departure was the hypothesis of a fault propagation phenomenon that
allows the observation of failure symptoms at a higher network level than the fault origin. Thus, for
the model's construction, monitoring data was collected from an extensive campus network in
which impact link failures were induced at different instants of time and with different duration.
These data correspond to widely used parameters in the actual management of a network. The
collected data allowed us to understand the faults' behavior and how they are manifested at a
peripheral level.
Based on this understanding and a data analytics process, the first three modules of our model,
named PALADIN, were proposed (Identify, Collection and Structuring), which define the data
collection peripherally and the necessary data pre-processing to obtain the description of the
network's state at a given moment. These modules give the model the ability to structure the data
considering the delays of the multiple responses that the network delivers to a single monitoring
probe and the multiple network interfaces that a peripheral device may have.
Thus, a structured data stream is obtained, and it is ready to be analyzed. For this analysis, it was
necessary to implement an incremental learning framework that respects networks' dynamic
nature. It comprises three elements, an incremental learning algorithm, a data rebalancing strategy,
and a concept drift detector. This framework is the fourth module of the PALADIN model named
Diagnosis.
In order to evaluate the PALADIN model, the Diagnosis module was implemented with 25 different
incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming scenario) as the rebalancing strategy. On the other hand, a dataset was built through the first
modules of the PALADIN model (SOFI dataset), which means that these data are the incoming data
stream of the Diagnosis module used to evaluate its performance.
The PALADIN Diagnosis module performs an online classification of network failures, so it is a
learning model that must be evaluated in a stream context. Prequential evaluation is the most used
method to perform this task, so we adopt this process to evaluate the model's performance over
time through several stream evaluation metrics.
RESULTS:
This research first evidences the phenomenon of impact fault propagation, making it possible to
detect fault symptoms at a monitored network's peripheral level. It translates into non-invasive
monitoring of the network. Second, the PALADIN model is the major contribution in the fault
detection context because it covers two aspects. An online learning model to continuously process
the network symptoms and detect internal failures. Moreover, the concept-drift detection and
rebalance data stream components which make resilience to dynamic network changes possible.
Third, it is well known that the amount of available real-world datasets for imbalanced stream
classification context is still too small. That number is further reduced for the networking context.
The SOFI dataset obtained with the first modules of the PALADIN model contributes to that number
and encourages works related to unbalanced data streams and those related to network fault
diagnosis.
CONCLUSIONS:
The proposed model contains the necessary elements for the continuous and timely diagnosis of IPbased
network faults; it introduces the idea of periodical monitorization of peripheral network
elements and uses data analytics techniques to process it. Based on the analysis, processing, and
classification of peripherally collected data, it can be concluded that PALADIN achieves the
objective. The results indicate that the peripheral monitorization allows diagnosing faults in the
internal network; besides, the diagnosis process needs an incremental learning process, conceptdrift
detection elements, and rebalancing strategy.
The results of the experiments showed that PALADIN makes it possible to learn from the network
manifestations and diagnose internal network failures. The latter was verified with 25 different
incremental algorithms, ADWIN as concept-drift detector and SMOTE (adapted to streaming
scenario) as the rebalancing strategy.
This research clearly illustrates that it is unnecessary to monitor all the internal network elements
to detect a network's failures; instead, it is enough to choose the peripheral elements to be
monitored. Furthermore, with proper processing of the collected status and traffic descriptors, it is
possible to learn from the arriving data using incremental learning in cooperation with data
rebalancing and concept drift approaches. This proposal continuously diagnoses the network
symptoms without leaving the system vulnerable to failures while being resilient to the network's
dynamic changes.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Molina López.- Secretario: Juan Carlos Dueñas López.- Vocal: Juan Manuel Corchado Rodrígue
A Novel Method for the Fault Diagnosis of a Planetary Gearbox based on Residual Sidebands from Modulation Signal Bispectrum Analysis
This paper presents a novel method for the fault diagnosis of planetary gearboxes based on an accurate estimation of residual sidebands using a modulation signal bispectrum (MSB). The residual sideband resulting from the out-phase superposition of vibration waves from asymmetrical multiple meshing sources are much less influenced by gear errors than that of the in-phase sidebands. Therefore, with the accurate estimation by MSB they can produce accurate and consistent diagnosis, which are evaluated by both simulating and experimental studies. However, the commonly used in-phase sidebands have high amplitudes but include gear error effects, consequently leading to poor diagnostic results
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