Jove
Visualize
Contact Us

Related Concept Videos

Stereotype Content Model02:16

Stereotype Content Model

15.6K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.6K
Survival Tree01:19

Survival Tree

462
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
462

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Tuning the Proportional-Integral-Derivative Control Parameters of Unmanned Aerial Vehicles Using Artificial Neural Networks for Point-to-Point Trajectory Approach.

Sensors (Basel, Switzerland)·2024
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 15, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

Statistical Feature Engineering for Robot Failure Detection: A Comparative Study of Machine Learning and Deep

Sertaç Savaş1

  • 1Department of Mechatronics Engineering, Erciyes University, 38039 Kayseri, Türkiye.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study compares machine learning and deep learning for robot failure detection using force-torque sensor data. Naive Bayes with raw time-series features achieved the highest accuracy, demonstrating the effectiveness of statistical features for robot failure classification.

Keywords:
classificationdeep learningfeature engineeringmachine learningrobot failure detectionstatistical features

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Related Experiment Videos

Last Updated: Mar 15, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.2K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Area of Science:

  • Robotics
  • Machine Learning
  • Sensor Data Analysis

Background:

  • Industrial robots are crucial in manufacturing, necessitating reliable failure detection for operational continuity.
  • Early and accurate identification of robot execution failures is vital for system reliability.

Purpose of the Study:

  • To comprehensively compare machine learning and deep learning methods for classifying robot execution failures.
  • To evaluate the impact of different feature engineering approaches on classification performance.
  • To identify the most effective algorithms and features for robot failure detection.

Main Methods:

  • Utilized force-torque sensor data from industrial robots.
  • Proposed three feature engineering approaches: Baseline (raw time-series), Domain-6 (basic statistics), and Domain-12 (comprehensive statistics).
  • Evaluated ten classification algorithms (8 ML, 2 DL) using nested and k-fold cross-validation over 30 runs.

Main Results:

  • Feature engineering significantly impacts classification performance.
  • Naive Bayes classifier with Baseline features achieved the highest accuracy (93.85% ± 0.90).
  • The Domain-12 feature set consistently improved performance across multiple algorithms.

Conclusions:

  • Time-domain statistical features are effective for robot failure classification.
  • Skewness features from Fx and Fy sensors were identified as critical for failure detection.
  • The study highlights the importance of feature engineering in optimizing robot failure detection systems.