AI-powered acoustic analysis for non-intrusive fault detection in BLDC motors of electric vehicles

Non-intrusive electrical and mechanical fault diagnosis of induction motors via Fourier-transform-aided learning

This paper proposes a knowledge-based input configuration to inform deep learning models for both electrical and mechanical fault diagnosis, rather than increasing model complexity, and confirms that, while conventional feature processing techniques perform well for electrical fault diagnosis, only the proposed FFT-informed input effectively captures both electrical and mechanical fault patterns.

From Simple Auditory Inspection to Acoustic Feature Extraction of Drone Brushless Motors

As the application of Unmanned Aerial Vehicles (UAVs) continues to expand globally, the operational health of propulsion components such as brushless motors is critical for ensuring flight safety. Traditional inspection typically relies on manual auditory diagnostics; however, this method is inherently subjective. Because human hearing sensitivity fluctuates across different frequencies, significant discrepancies often exist between objective sound pressure levels and human perception—especially at frequency extremes. Consequently, the reliability and consistency of such auditory-based fault detection are frequently scrutinized. This research establishes an objective, multi-dimensional acoustic feature extraction framework to provide scientific, quantified data that supports human judgment, thereby enhancing diagnostic accuracy. The methodology integrates time-domain, Envelope Analysis, and Time Synchronous Averaging (TSA) techniques to extract key signal features. Analysis of a specific audio sample revealed a signal dominated by intense high-frequency noise peaking at 5,871.09 Hz, exhibiting sharp impulsive characteristics with a Crest Factor (CF) of 4.23. Following TSA processing, asynchronous noise was attenuated by approximately 84.3%, successfully isolating a periodic impact signal at 40.28 Hz, which is precisely synchronous with the shaft rotation speed. The resulting CF of 3.31 confirms the presence of regular, persistent impacts, suggesting potential bearing looseness. The proposed framework effectively isolates weak, fault-related signals from high-intensity noise environments. These objective, quantified results provide a robust scientific basis to assist operators in making more consistent and precise assessments of UAV health. Future research will focus on expanding the experimental dataset and integrating machine learning models to develop a fully automated diagnostic system.

Multi-Sensor Fusion and Frequency-Domain Analysis for Predictive Maintenance of Industrial Induction Motors

Unplanned failures of induction motors impose serious operational and financial penalties on industrial facilities, yet the fault signatures that precede such failures are detectable well in advance through careful sensor instrumentation and data-driven analysis. This paper presents an end-to-end Internet-of-Things (IoT) predictive maintenance scheme based on two off-the-shelf sensors: a DS18B20 one-wire digital thermometer and 2 piezo vibration sensor modules, with an ESP32 edge device for running the full machine learning pipeline offline, independent from any cloud services. Four operating scenarios are considered: healthy condition, BPFO (bearing outer race fault), misaligned shaft, and rotor imbalance. Based on 1-second sampling intervals, 17 descriptors are derived, including statistics in the time domain, Fourier harmonic peaks, energy ratios between different frequency bands, and temperature gradients measured across all sensors. A dual-stage feature selection method using mutual information (MI) score and Random Forest mean decrease impurity (MDI) ranking reduces the number of features to the 10 most relevant descriptors, reducing the computational complexity by 41% at the expense of 5.6% F1-macro. On a balanced 600-sample synthetic dataset, the resulting Random Forest classifier attains 87.3% hold-out accuracy, 91.0±1.9% five-fold cross-validation accuracy, and a macro area-under-the-ROC-curve of 0.980. End-to-end inference takes just 39 ms on the ESP32, easily meeting the 200 ms requirement for real-time alerting.

Research on Fault Feature Extraction and Intelligent Diagnosis Technology of Motor Drive in Embedded Systems

Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.

Fault Detection and Isolation in Electric Vehicle Powertrain

Robust Open-Circuit Fault Diagnosis of PMSMs Using Feature Fusion of Current Signatures With Deep Neural Networks

Permanent magnet synchronous motors (PMSMs) are broadly used in diverse applications due to their inherent advantages. Open-circuit faults (OCFs) are among the major fault classifications in PMSMs, posing significant concerns due to their contribution to torque ripples, vibrations, and efficiency degradation. Therefore, accurate and real-time OCF diagnosis is essential for reliable operation and predictive maintenance practices. This underscores the importance of a robust diagnostic framework that enables early fault detection and localization, supports embedded integration, and requires no additional dedicated sensors. However, existing studies rarely address these requirements together. To overcome these limitations, this article proposes a novel OCF diagnostic framework that fuses features derived from multiple strategies, including wavelet energy-based features, frequency-domain features extracted from current waveforms, and speed measurement data. The extracted feature vector is used as input to a lightweight deep neural network. The proposed approach enhances interpretability and enables seamless embedded integration compared to conventional raw-data-driven machine learning models. In addition, an extended refinement layer is incorporated to enable integrated fault detection and classification for OCF while enhancing diagnostic transparency. The effectiveness of the proposed method is demonstrated through MATLAB/Simulink simulations using the PLECS Blockset and further validated in real-time with an RTBox-based hardware-in-the-loop setup using a C2000 launchpad. Furthermore, experimental validation is conducted using a domain-adaptation strategy based on transfer learning. Performance evaluation confirms diagnostic accuracy exceeding 99% across varying operating conditions. The validation process achieves fault detection within 22% of a fundamental electrical cycle, with fault localization occurring within 40% of an average, demonstrating the robustness and adaptability of the proposed method. A sensitivity analysis of the proposed algorithm’s feature vector validates the effectiveness of high-frequency features. Furthermore, the risk distribution matrix provides insights supporting informed maintenance decisions.