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Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations

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A Correction to this article was published on 11 February 2022

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Abstract

In the age of Industry 4.0, multi-axis robots are widely used in smart manufacturing thanks to their capacity of milling high complex forms and interacting with several systems in production lines. However, during manufacturing, the occurrence of small drifts in the robot arms may lead to critical failures and significant product quality damages and, therefore, high financial losses. Hence, this paper aims to develop an effective and practical methodology for online diagnostics of robot drifts based on information fusion of direct and indirect monitoring. The direct monitoring exploits the already installed encoders on each servomotor of the robot while the indirect monitoring uses heterogeneous sensors (current, vibration, force and torque) placed at the robot tool level. The sensor measurements of the robot tool are processed, in an offline phase, to build health indicators and fused to learn a classifier for drifts detection and diagnostics. Then, during the online phase and in the case of presence of new drift patterns, the encoder measurements are used to label these patterns and update the classifier learned previously to diagnose their origin. The efficiency and robustness of the proposed methodology are verified through a real industrial machining multi-axis robot that investigates different drift severities of its arms.

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Change history

  • 14 February 2022

    The original online version of this article was revised: Author name Kamal Medjaher has been corrected.

  • 11 February 2022

    A Correction to this paper has been published: https://doi.org/10.1007/s10845-022-01919-y

Abbreviations

HIs :

Health indicators of the indirect monitoring

\(HIs^{\prime }\) :

Normalized HIs

\(\bar{HIs^{\prime }}\) :

Mean value of indirect monitoring \(HIs^\prime \)

RMS :

Root Mean Square

StD, \(\sigma \) :

Standard Deviation

VAR :

Variance

KUR :

Kurtosis

\(y_{h}\) :

Raw signal segment

\(y^{\prime }_{h}\) :

Normalized raw signal segment

L :

Length of each signal segment \(y_{h}\)

FFT :

Fast Fourier Transform

Y :

Global raw signal

Ne :

Number of signal segments

h :

Ensemble of observations of each segment Ne

\(\varOmega \) :

Class of observations of indirect monitoring

AE :

Auto-Encoder

f :

Encoding function of the AE

g :

Decoding function of the AE

w, b :

Weights and bias of the encoding layer

\(w', b'\) :

Transposed w and b of the decoding layer

\(O_{1}\) :

\(HIs^{\prime }\) input observations to the AE

\(O_{2}\) :

Fused \(HIs^{\prime }\) through hidden layer encoding

\(O_{3}\) :

Reconstructed \(HIs^{\prime }\) of AE output layer

MSE :

Mean Square Error of \(O_1\) and \(O_3\)

\(D_{O_{2}}\) :

Euclidean distance of \(HIs^{\prime }\) to class’s centroïd

\(\bar{D_{O_{2}}}\) :

Mean of Euclidean distance of \(D_{O_{2}}\)

\(P_{thr}\) :

Peripheral threshold of each \(HIs^{\prime }\) class

C :

Centroïds of indirect monitoring

n :

Number of encoder data observations

error :

Position error of robot’s axes

\(\bar{error}\) :

Mean of position error of robot’s axes

s :

System health state

\(Indi_{i}\) :

Direct monitoring indicator of the ith axis

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Acknowledgements

The project has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain France Andorra programme (POCTEFA 2014-2020). POCTEFA aims to reinforce the economic and social integration of the French-Spanish-Andorran border. Its support is focused on developing economic, social and environmental cross-border activities through joint strategies favouring sustainable territorial development.

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Authors and Affiliations

  1. FEMTO-ST Institute, Université de Bourgogne Franche-Comté CNRS/UFC/ENSMM, 26 rue de l’Épitaphe, 25000, Besançon, France

    Moncef Soualhi

  2. Production Engineering Laboratory, Toulouse University INPT-ENIT, 47 Av. d’Azereix, 65000, Tarbes, France

    Moncef Soualhi, Khanh T. P. Nguyen & Kamal Medjaher

  3. Technology Transfer Center, METALLICADOUR, 1 cours de l’Industrie, 64510, Assat, France

    Denis Lebel & David Cazaban

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  1. Moncef Soualhi
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  2. Khanh T. P. Nguyen
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  3. Kamal Medjaher
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  4. Denis Lebel
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  5. David Cazaban
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Correspondence to Moncef Soualhi.

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The original online version of this article was revised: The author name Kamal Medjaher has been corrected.

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Soualhi, M., Nguyen, K.T.P., Medjaher, K. et al. Intelligent monitoring of multi-axis robots for online diagnostics of unknown arm deviations. J Intell Manuf 34, 1743–1759 (2023). https://doi.org/10.1007/s10845-021-01882-0

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  • DOI: https://doi.org/10.1007/s10845-021-01882-0

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