Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations

Serdio, Francisco; Lughofer, Edwin; Pichler, Kurt; Buchegger, Thomas; Pichler, Markus; Efendic, Hajrudin

doi:10.1016/j.inffus.2014.03.006

Cited by 88 publications

(42 citation statements)

References 41 publications

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“…The paper [34] presents some methods for training pattern (prototype) selection, class-specific feature selection and classification for automated learning. In [39], the authors introduce the usage of multivariate orthogonal space transformations and vectorized time-series models in combination with data-driven system identification models to achieve an enhanced performance of residualbased fault detectionȦ novel approach for time series forecasting based on ordered weighted averaging operators as linear filter and forecasting models is addressed in [5]. In [10] and [43], acquisition systems to obtain real data of wind turbines and wind farms are used for the fault detection.…”

Section: Introductionmentioning

confidence: 99%

MSAFIS: an evolving fuzzy inference system

Rubio

Bouchachia

2015

Soft Comput

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In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), uniform stable backpropagation algorithm (SBP). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SBP is used instead of the Kalman filter for the updating of parameters. The SBP has the advantage that is obtains a better learning than the Kalman filter in big data. The effectiveness of the studied methods is verified by two experiments.

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Section: Introductionmentioning

confidence: 99%

MSAFIS: an evolving fuzzy inference system

Rubio

Bouchachia

2015

Soft Comput

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“…One example includes the learning of one-class SVM, which requires non-labeled data [18,19]. Other studies also utilized the method of non-labeled data, hence being able to operate in a fully unsupervised manner [20,21]. Fuzzy analytical hierarchy process was used to select unstable slicing machines to control wafer slicing quality, where the results of exponentially weighted moving average control chart demonstrated the feasibility of the proposed algorithm in effectively selecting the evaluation outcomes and evaluating the precision of the worst performing machines [22].…”

Section: Literature Reviewmentioning

confidence: 99%

E-quality control: A support vector machines approach

Tseng

Aleti

et al. 2015

Journal of Computational Design and Engineering

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James) Kwon, E-quality control: A support vector machines approach, Journal of Computational Design and Engineering, http://dx. AbstractThe automated part quality inspection poses many challenges to the engineers, especially when the part features to be inspected become complicated. A large quantity of part inspection at a faster rate should be relied upon computerized, automated inspection methods, which requires advanced quality control approaches. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of support vector machine (SVM) learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments and analyzed with different kernel functions. From the analysis, detailed outcome is presented for six different cases. The results indicate the robustness of support vector classification for the experimental data with two output classes.

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“…There are also fault diagnosis methods in multi-sensor networks. As another efficient method to fault diagnosis, the residual-based unsupervised fault diagnosis method addresses uncertainty in the form of error bars around the soft computing models extracted from data [15][16][17]. Multi-sensor data fusion fault diagnosis [18,19], which is widely used in sensor design [20,21], as a data-driven approach, has been paid more and more attention.…”

Section: Introductionmentioning

confidence: 99%

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

Jiang

Xie

2017

Applied Sciences

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Fault diagnosis is an important research direction in modern industry. In this paper, a new fault diagnosis method based on multi-sensor data fusion is proposed, in which the Dempster-Shafer (D-S) evidence theory is employed to model the uncertainty. Firstly, Gaussian types of fault models and test models are established by observations of sensors. After the models are determined, the intersection area between test model and fault models is transformed into a set of BPAs (basic probability assignments), and a weighted average combination method is used to combine the obtained BPAs. Finally, through some given decision making rules, diagnostic results can be obtained. The proposed method in this paper is tested by the Iris data set and actual measurement data of the motor rotor, which verifies the effectiveness of the proposed method.

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Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations

Cited by 88 publications

References 41 publications

MSAFIS: an evolving fuzzy inference system

MSAFIS: an evolving fuzzy inference system

E-quality control: A support vector machines approach

A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion

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