Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Yordanov, Nikolay(2)
• Zhilevski, Marin(2)
• Benedicenti, L.(1)
• Ertargin, Merve(1)
• Ertarğın, Merve(1)
• Liu, Z.(1)
• Mikhov, Mikho(1)

• BildiriYalnız künye2025Scopus SJRQ4/r/munzur/2309
  A Hybrid Machine Learning Architecture for Acoustic Fault Diagnosis in Electric Motors: Offline and Online Modes via Mobile Phone

  Yordanov, Nikolay; Zhilevski, Marin; Mikhov, Mikho; Ertargin, Merve

  This paper proposes a hybrid ML architecture for non-invasive acoustic fault diagnosis in electric motors and machines using a.NET MAUI cross-platform mobile app for short audio capture (5-20s), audio-to-image transformations and dual-mode inference: embedded ML.NET/ONNX models for offline preliminary detection and Azure ML-hosted deep CNN/RNN for online probabilistic multi-fault analysis. The primary objective of this paper is to demonstrate the benefits of the proposed architecture and to show the specific tools which can be used for implementation. Architectures (embedded, cloud, hybrid) are evaluated by listing main advantages and disadvantage, highlighting hybrid approach that combines offline resilience, reduced latency and keeping the possibility for complex analysis by using online hosted model. Sample mobile application design is added to showcase the proposed user experience.

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• BildiriAçık Erişim2025/r/munzur/1908
  Health Status Classification of Electric Motors Using CNN-Based Models and SDP Images Under Varying Noise Conditions

  Ertarğın, Merve; Yordanov, Nikolay; Zhilevski, Marin; Benedicenti, L.; Liu, Z.

  The increasing application of electric motors across various industrial sectors requires effective monitoring and early diagnostics to prevent potential failures. This study explores the use of Convolutional Neural Network (CNN)-based models together with Symmetrized Dot Pattern (SDP) sound representations for classifying the health status of electric motors under different noise conditions. Acoustic data from a brushless DC motor were transformed into SDP images, which were then used to train CNN models. The dataset included recordings of motors in “Good”, “Broken” and “Heavy Load” conditions, captured under various noise environments such as pure, talking, white noise, atmospheric, and stress test conditions. The classification tasks were conducted under three conditions: assessing motor health status, evaluating both motor health status and noise types, and excluding the stress test noise type for a balanced dataset. The results demonstrated that the CNN models achieved high accuracy rates in classifying motor health status, with the Custom CNN model performing best in simpler tasks and MobileNet excelling in more complex scenarios. The study highlights the feasibility of using SDP images with CNN-based models for fault classification in motors and suggests future research directions for improving classification accuracy through advanced feature extraction techniques and multimodal data representations.

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