ArticleSensors (Basel, Switzerland)2021
Smart Anomaly Detection and Prediction for Assembly Process Maintenance in Compliance with Industry 4.0.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry.Sensors (Basel, Switzerland) · 2022Pooled it
- A Multimodal TinyML-Based Predictive Maintenance Architecture for Industrial IoT in the 6G Era.Sensors (Basel, Switzerland) · 2026Article
- RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines.Sensors (Basel, Switzerland) · 2024Article
- Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques.PeerJ. Computer science · 2024Article
- Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors.Sensors (Basel, Switzerland) · 2022Article
- Robust Assembly Assistance Using Informed Tree Search with Markov Chains.Sensors (Basel, Switzerland) · 2022Article
- Identifying and Characterizing Conveyor Belt Longitudinal Rip by 3D Point Cloud Processing.Sensors (Basel, Switzerland) · 2021Article
- Analyzing Particularities of Sensor Datasets for Supporting Data Understanding and Preparation.Sensors (Basel, Switzerland) · 2021Article
- Sound Detection Monitoring Tool in CNC Milling Sounds by K-Means Clustering Algorithm.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
One of the big problems of today's manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.