Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics
The use of a modern technological system requires a good engineering approach,
optimized operations, and proper maintenance in order to keep the system in an optimal …
optimized operations, and proper maintenance in order to keep the system in an optimal …
Mechanical fault detection based on machine learning for robotic RV reducer using electrical current signature analysis: A data-driven approach
Recently, prognostic and health management (PHM) has become a prominent field in
modern industry. The rotate vector (RV) reducer is one of the widely used mechanical …
modern industry. The rotate vector (RV) reducer is one of the widely used mechanical …
In-situ fault diagnosis for the harmonic reducer of industrial robots via multi-scale mixed convolutional neural networks
The faults of harmonic reducers result in excessive vibration affecting the joint stabilization of
industrial robots and manufacturing quality. In-situ fault diagnosis of harmonic reducers can …
industrial robots and manufacturing quality. In-situ fault diagnosis of harmonic reducers can …
Autonomous grinding algorithms with future prospect towards SMART manufacturing: A comparative survey
Autonomous Grinding (AG) is a unique kind of manufacturing process which needs extra
operative care, for both robot and environment. Due to the rapid growth of research on AG, a …
operative care, for both robot and environment. Due to the rapid growth of research on AG, a …
Multirobot collaborative task dynamic scheduling based on multiagent reinforcement learning with heuristic graph convolution considering robot service performance
To address the problem of multirobot collaborative task scheduling considering the
degradation of industrial robot performance and the recovery of robot performance through …
degradation of industrial robot performance and the recovery of robot performance through …
Phase-based time domain averaging (PTDA) for fault detection of a gearbox in an industrial robot using vibration signals
This paper proposes a fault detection method that uses vibration signals in the gearboxes of
industrial robots. The vibration signals from gearboxes consist of both deterministic signals …
industrial robots. The vibration signals from gearboxes consist of both deterministic signals …
[HTML][HTML] Data-driven drift detection and diagnosis framework for predictive maintenance of heterogeneous production processes: Application to a multiple tap** …
The rise of Industry 4.0 technologies has revolutionized industries, enabled seamless data
access, and fostered data-driven methodologies for improving key production processes …
access, and fostered data-driven methodologies for improving key production processes …
Supervised machine-learning methodology for industrial robot positional health using artificial neural networks, discrete wavelet transform, and nonlinear indicators
Robotic systems are a fundamental part of modern industrial development. In this regard,
they are required for long periods, in repetitive processes that must comply with strict …
they are required for long periods, in repetitive processes that must comply with strict …
Prediction for manufacturing factors in a steel plate rolling smart factory using data clustering-based machine learning
A Steel Plate Rolling Mill (SPM) is a milling machine that uses rollers to press hot slab inputs
to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is …
to produce ferrous or non-ferrous metal plates. To produce high-quality steel plates, it is …
Towards manufacturing robotics accuracy degradation assessment: A vision-based data-driven implementation
In this manuscript we report on a vision-based data-driven methodology for industrial robot
health assessment. We provide an experimental evidence of the usefulness of our …
health assessment. We provide an experimental evidence of the usefulness of our …