Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

Serdio, Francisco; Lughofer, Edwin; Zăvoianu, Alexandru-Ciprian; Pichler, Kurt; Pichler, Markus; Buchegger, Thomas; Efendic, Hajrudin

doi:10.1016/j.asoc.2016.11.038

Cited by 38 publications

(25 citation statements)

References 43 publications

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“…Just as an example, a famous digital filter for smoothing the data is Savitzky-Golay filter [96,81,84], which is based on a local low-order polynomial interpolation using for each point a window containing some of its neighbor points. Some filters are also suitable for incremental on-line applica-tion on a streaming context [102].…”

Section: Signal Processingmentioning

confidence: 99%

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Cernuda

2019

Predictive Maintenance in Dynamic Systems

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The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way.We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems.

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Section: Signal Processingmentioning

confidence: 99%

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Cernuda

2019

Predictive Maintenance in Dynamic Systems

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“…Interpretability of FRBSs is also taken into account (Rudzinski, 2016) by means of MOEAs this past year. Interval-valued fuzzy kNN have been also obtained by means of CHC optimization (Derrac et al, 2016), as well as sparse fuzzy inference systems were obtained by the application of a memetic GA (Serdio et al, 2017). Multi-objective GP is also used to perform symbolic regression for imbalanced classification (Bhowan et al, 2013).…”

Section: Classification (Second Period)mentioning

confidence: 99%

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Alcalá

Gacto

Alcalá‐Fdez

2017

WIREs Data Min & Knowl

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The ability of evolutionary algorithms (EAs) to manage a set of solutions, even attending multiple objectives, as well as their ability to optimize any kinds of values, allows them to fit very well some parts of the data‐mining (DM) problems, whose native learning techniques usually associated with the inherent DM problem are not able to solve. Therefore, EAs are widely applied to complement or even replace the classical DM learning approaches. This application of EAs to the DM process is usually named evolutionary data mining (EDM). This contribution aims at showing a glimpse of the EDM field current state by focusing on the most cited papers published in the last 10 years. A descriptive analysis of the papers together with a bibliographic study is performed in order to differentiate past and current trends and to easily focus on significant further developments. Results show that, in the case of the most cited studied papers, the use of EAs on DM tasks is mainly focused on enhancing the classical learning techniques, thus completely replacing them only when it is directly motivated by the nature of problem. The bibliographic analysis is also showing that even though EAs were the main techniques used for EDM, the emergent evolutionary computation algorithms (swarm intelligence, etc.) are becoming nowadays the most cited and used ones. Based on all these facts, some potential further directions are also discussed. WIREs Data Mining Knowl Discov 2018, 8:e1239. doi: 10.1002/widm.1239 This article is categorized under: Fundamental Concepts of Data and Knowledge > Knowledge Representation Technologies > Computational Intelligence Technologies > Classification Technologies > Prediction

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“…The first-class includes the detection of single faults by analyzing one or multiple parameters; the second class covers the detection of different faults with multiple parameters and processing techniques, and the last one contains the mixed techniques of various computing-intensive approaches to analyze different electrical and mechanical parameters in order to detect multiple faults [61][62][63][64]. In contrast to conventional signal processing based fault detection techniques [65], recently a few attempts are made for the application of intelligent algorithms [66,67] including new approaches to fault detection and isolation (FDI) [68] based on fuzzy logic, decision trees, neural networks, and further machine learning techniques [69][70][71][72][73]. However, most of them rely on the measurement and processing of vibration signals, which require at least one vibration sensor, which demands extra costs for its proper installation and maintenance [74][75][76][77].…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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Improved fault detection employing hybrid memetic fuzzy modeling and adaptive filters

Cited by 38 publications

References 43 publications

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017)

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

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