Sensor fusion for more accurate features in condition monitoring of mechatronic systems

D’Emilia, Giulio; Gaspari, Antonella; Natale, Emanuela

doi:10.1109/i2mtc.2019.8826848

Cited by 7 publications

(11 citation statements)

References 14 publications

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“…The methodology has been implemented with reference to an automatic system in real scale (Figure 1a), able to realize an alternate linear motion along a vertical axis by means of one ball screw. One motor manages the motion, so the movement of the shaft is a pure translation, with sinusoidal motion law at a frequency in the range up to 5 Hz and a stroke up to 150 mm [17].…”

Section: Methodsmentioning

confidence: 99%

“…Realization of experiments, corresponding to different operating conditions.Multiple runs of the model, considering as input different quantities, theoretical or measured ones [17,28]. The possible inputs are: Angular position of the motor axis,Angular velocity of the motor axis,Electric current at the driving servomotor,Linear acceleration of the ball screw shaft,Linear velocity of the ball screw shaft.Comparison between outputs of the model and data deriving from measurements (angular position or velocity of the motor axis from the encoder, electric current at the driving servomotor from the internal sensor, linear acceleration of the ball screw shaft from the accelerometer, linear velocity of the ball screw shaft from the LDV).Calculation and selection of the most suitable features for the jload setting [17] and the lubrication state identification. Jload is a parameter of the control system, representing the load inertia at the motor axis.Application of advanced data processing techniques for classification.…”

Section: Methodsmentioning

confidence: 99%

“…Multiple runs of the model, considering as input different quantities, theoretical or measured ones [17,28]. The possible inputs are: Angular position of the motor axis,Angular velocity of the motor axis,Electric current at the driving servomotor,Linear acceleration of the ball screw shaft,Linear velocity of the ball screw shaft.…”

Section: Methodsmentioning

confidence: 99%

“…In a previous work [17], the authors aim at improving the classification capability of features, by using an integrated approach of different contributions. Sensor fusion has been used to have experimental information at different positions of the mechanism and to merge their experimental information to the indications of a simulation model of the kinematic and dynamic behavior of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Due to these many reasons, feature selection is a key point of the CM strategy. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%