ArticleSensors (Basel, Switzerland)2023
The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- A Review of Fault Diagnosis and Intelligent Operations and Maintenance for Agricultural Machinery: Fault Mechanisms, Key Technologies, and Practical Recommendations.Sensors (Basel, Switzerland) · 2026Review
- A Physics-Guided Dual-Sensor Framework for Bearing Fault Diagnosis in PMDC Motor Drives.Sensors (Basel, Switzerland) · 2026Article
- Motor Temperature Observer for Four-Mass Thermal Model Based Rolling Mills.Sensors (Basel, Switzerland) · 2025Article
- Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2024Article
- Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.
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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.