This journal publishes research on the diagnosis and detection of faults and wear in industrial machinery and electrical systems. Articles focus on developing advanced methods, often utilizing machine learning and signal processing techniques, to improve accuracy, efficiency, and robustness in identifying issues like high impedance faults, tool wear, bearing degradation, and electrical anomalies.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Biomedical EngineeringQ3
Electrical and Electronic EngineeringQ3
Mechanical EngineeringQ3
Safety, Risk, Reliability and QualityQ3
Signal ProcessingQ3
SoftwareQ3
Subject Classification
Scopus Categories
Biomedical EngineeringSoftwareElectrical and Electronic EngineeringSafety, Risk, Reliability and QualitySignal ProcessingMechanical Engineering
Research Topics (OpenAlex)
Technical Engine Diagnostics and MonitoringAdvancements in Materials EngineeringMechanical Engineering Research and ApplicationsEngine and Fuel EmissionsMining and Industrial ProcessesMechanical and Thermal Properties AnalysisTransportation Systems and SafetyEngineering Diagnostics and ReliabilityMachine Fault Diagnosis TechniquesStructural Health Monitoring Techniques