Failure detection in robotic arms using  statistical modeling, machine learning and hybrid gradient boosting

• Modeling and failure prediction is an important task in manyengineering systems. For this task, the machine learning literaturepresents a large variety of models such as classification trees,random forest, artificial neural networks, fuzzy systems, amongothers. In addition, standard statistical models can be applied suchas the logistic regression, linear discriminant analysis, $k$-nearestneighbors, among others. This work evaluates advantages andlimitations of statistical and machine learning methods to predictfailures in industrial robots. The work is based on data from morethan five thousand robots in industrial use. Furthermore, a newapproach combining standard statistical and machine learning models,named \emph{hybrid gradient boosting}, is proposed. Results show thatthe a priori treatment of the database, i.e., outlier analysis,consistent database analysis and anomaly analysis have shown to becrucial to improve classification performance for statistical, machinelearning and hybrid models. Furthermore, local joint information hasbeen identified as the main driver for failure detection whereasfailure classification can be improved using additional informationfrom different joints and hybrid models.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)

Keyword

Failure detection in robotic arms using statistical modeling

machine learning and hybrid gradient boosting

Publication and Content Type

vet (subject category)

rap (subject category)