A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Hu, Yang; Baraldi, Piero; Maio, Francesco Di; Zio, Enrico

doi:10.1016/j.ymssp.2016.11.004

Cited by 31 publications

(16 citation statements)

References 48 publications

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“…Notice that in many industrial applications ≫ , which entails that the number of training examples, , is too small for an accurate estimate of the parameters of a classifier with in input all the extracted features. Further, it is known that many irrelevant features unnecessarily increase the complexity of the classification problem and can degrade the classification performance [7], [17]. Thus, the development of the diagnostic classifier is generally preceded by feature selection.…”

Section: Proposed Methodologymentioning

confidence: 99%

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Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

Cannarile¹,

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Baraldi

et al. 2019

Aerospace Science and Technology

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The objective of this work is the development of a fault diagnostic system for a shaker blower used in on-board aeronautical systems. Features extracted from condition monitoring signals and selected by the ELastic NET (ELNET) algorithm, which combines 1 -penalty with the squared 2 -penalty on model parameters, are used as inputs of a Multinomial Logistic regression (MLR) model. For validation, the developed approach is applied to experimental data acquired on a shaker blower system (as representative of aeronautical on-board systems) and on three additional experimental datasets of literature. The satisfactory diagnostic performances obtained show the potential of the method for developing sound diagnostic classifiers from a very large set of features, even when only few training data are available.

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Section: Proposed Methodologymentioning

confidence: 99%

“…By Lagrangian duality, there is a one-to-one correspondence between the constrained optimization problem in Eq. (7) and its Lagrangian form [20], [22]:…”

Section: Elastic Net Multinomial Logistic Regressionmentioning

confidence: 99%

Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

Cannarile¹,

Compare

Baraldi

et al. 2019

Aerospace Science and Technology

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show abstract

“…In many cases this can be challenging due to the imbalanced nature of the data. Since there are usually only a handful of defects per million pieces, there is much more information about the normal process regime than there is about the faulty one [11,12]. M2M communication can contribute to improving the performance of the learned model through sharing the data among similar work systems to increase the overall volume.…”

Section: Fault Diagnostics Employing M2m Communicationmentioning

confidence: 99%

Knowledge elicitation for fault diagnostics in plastic injection moulding: A case for machine-to-machine communication

2017

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In most manufacturing processes the defect rate is very low. Sometimes, only a few parts per million are defective because of a faulty process. For this reason, fault diagnostics is faced with extremely imbalanced data sets and requires large volumes of data to achieve a reasonable performance. This paper explores whether a machine-to-machine approach can be used, in which several work systems share the process data to improve the accuracy of the faultdetection model. The model is based on machine learning and is applied to industrial data from approximately two million process cycles performed on several injection moulding work systems.

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“…However, this issue is rarely discussed in the field of mechanical fault diagnosis. In practice, it is laborious or expensive to collect faulty samples with labels, whereas the unlabeled samples are abundant [ 13 , 14 ]. Therefore, it is valuable to develop semi-supervised methods for fault diagnosis to improve the accuracy as much as possible by means of a few labeled data and mass unlabeled data.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

Chen

Wang

Zhang

et al. 2018

Sensors

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Fault diagnosis of rolling element bearings is an effective technology to ensure the steadiness of rotating machineries. Most of the existing fault diagnosis algorithms are supervised methods and generally require sufficient labeled data for training. However, the acquisition of labeled samples is often laborious and costly in practice, whereas there are abundant unlabeled samples which also imply health information of bearings. Thus, it is worthwhile to develop semi-supervised methods of fault diagnosis to make effective use of the plentiful unlabeled samples. Nevertheless, considering the normal data are much more than the faulty ones, the problem of imbalanced data exists among unlabeled samples for fault diagnosis. Besides, in practice, bearings often work under uncertain and variable operation conditions, which would also have negative influence on fault diagnosis. To solve these issues, a novel hybrid method for bearing fault diagnosis is proposed in this paper: (1) Inspired by visibility graph, a novel fault feature extraction method named visibility graph feature (VGF) is proposed. The obtained features by VGF are natively insensitive to variable conditions, which has been validated by a simulation experiment in this paper; (2) On basis of VGF, to deal with imbalanced unlabeled data, graph-based rebalance semi-supervised learning (GRSSL) for fault diagnosis is proposed. In GRSSL, a graph based on a weighted sparse adjacency matrix is constructed by the k-nearest neighbors and Gaussian Kernel weighting algorithm by means of the samples. Then, a bivariate cost function over classification and normalized label variable is built up to rebalance the importance of labels. Finally, the proposed VGF-GRSSL method was verified by data collected from Case Western Reserve University Bearing Data Center. The experiment results show that the proposed method of bearing fault diagnosis performs effectively to deal with the imbalanced unlabeled data under variable conditions.

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A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Cited by 31 publications

References 48 publications

Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

Knowledge elicitation for fault diagnostics in plastic injection moulding: A case for machine-to-machine communication

A Semi-Supervised Approach to Bearing Fault Diagnosis under Variable Conditions towards Imbalanced Unlabeled Data

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