Feature evaluation and selection for condition monitoring using a self-organizing map and spatial statistics

Silva, Rui; Wilcox, S. J.

doi:10.1017/s0890060417000518

Cited by 15 publications

(12 citation statements)

References 35 publications

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“…Many contributions regarding the detection of tool wear levels have been proposed, developed, and tested to take a meaningful decision, useful and important features. But most research absence the robustness which is essential for industrial applications primarily because of limited scope regarding machining conditions, and deficient signal processing or poor feature selection [4]. The present study focuses on feature extraction methods based on the time domain, frequency domain, WT, EMD, and multi-domain analysis [5].…”

Section: Introductionmentioning

confidence: 99%

Cutting tool wear progression index via signal element variance

Kasim

Nuawi

Ghani

et al. 2019

JMES

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This paper presents a new statistical-based method of cutting tool wear progression in a milling process called Z-rotation method in association with tool wear progression. The method is a kurtosis-based that calculates the signal element variance from its mean as a measurement index. The measurement index can be implicated to determine the severity of wear. The study was conducted to strengthen the shortage in past studies notably considering signal feature extraction for the disintegration of non-deterministic signals. The Cutting force and vibration signals were measured as a tool of sensing element to study wear on the cutting tool edge at the discrete machining conditions. The monitored flank wear progression by the value of the RZ index, which then outlined in the model data pattern concerning wear and number of samples. Throughout the experimental studies, the index shows a significant degree of nonlinearity that appears in the measured impact. For that reason, the accretion of force components by Z-rotation method has successfully determined the abnormality existed in the signal data for both force and vibration. It corresponds to the number of cutting specifies a strong correlation over wear evolution with the highest correlation coefficient of R2 = 0.8702 and the average value of R2 = 0.8147. The index is more sensitive towards the end of the wear stage compared to the previous methods. Thus, it can be utilised to be the alternative experimental findings for monitoring tool wear progression by using threshold values on certain cutting condition.

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Section: Introductionmentioning

confidence: 99%

Cutting tool wear progression index via signal element variance

Kasim

Nuawi

Ghani

et al. 2019

JMES

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“…In fact, SOMs have been widely used for condition monitoring applications in other contexts [ 29 , 30 ]. In this manuscript, an original KPI based on the frequency of cells occupancy has been introduced on purpose for our specific application of interest.…”

Section: Methodsmentioning

confidence: 99%

Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps

Betti¹,

Tucci

Crisostomi

et al. 2021

Sensors

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In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

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“…The features that are traditionally used for CM (RMS, crest factor, kurtosis, …, in the time domain; amplitude of power spectra, band power, envelope, …, in the frequency domain) [4,12,13,14,19,20,21,22,23,24,25], and that are considered in this work, are useful in most applications to maintain the relevant information about the process or tool conditions [4].…”

Section: Methodsmentioning

confidence: 99%

“…A first selection criterion of the features based on their repeatability was proposed [17]. The debate about these topics is very active, with reference to both general approach and specific applications, like, for instance, those related to tool CM in production machine [19,20,21,22] or computer vision applications [23]. How much these methods are of general validity is still an open point for CM of mechatronic systems.…”

Section: Introductionmentioning

confidence: 99%

Physical and Metrological Approach for Feature’s Definition and Selection in Condition Monitoring

D’Emilia

Gaspari

Natale

2019

Sensors

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In this paper, a methodology is described aiming at emphasizing physical and metrological criteria in feature selection for condition monitoring of a real scale mechatronic system. The device is used for packaging applications according to the movements of its end effector, driven by a couple of brushless servomotors and a kinematic mechanical linkage. The approach is hybrid, meaning that the starting feature set is built with reference to both experimental data from different sensors and to the indication of a simplified kinematic and dynamic model of the mechanical linkage itself. A critical comparison and mixing of theoretical and experimental data, based also on a physical interpretation of differences, suggests some more features, with respect to the classical ones, of hybrid type, which could be mostly correlated to the effects of statuses and defects of the system to be identified. The whole procedure is step by step validated, in order to evaluate the variability of features, throughout the whole procedure. The variability is analyzed depending on the actions that are realized in order to define, select, and use the proposed features for data processing by advanced algorithms, like the most typically used classifiers and artificial neural networks. A comparison with the state-of-the-art automatic feature’s selection procedure is also presented. Experimental results show that the proposed methodology is able to classify with high accuracy many statuses of the mechatronic system, which are only slightly different as for set-up settings and/or mechanical wear and lubrication conditions of mechanical parts of the mechatronic system. Issues to be pursued to a more effective generalization of the method are also discussed.

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Feature evaluation and selection for condition monitoring using a self-organizing map and spatial statistics

Cited by 15 publications

References 35 publications

Cutting tool wear progression index via signal element variance

Cutting tool wear progression index via signal element variance

Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps

Physical and Metrological Approach for Feature’s Definition and Selection in Condition Monitoring

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