Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 774-779· 0 citations· 17 references
Abstract
The effective and dependable functioning of high-speed permanent-magnet brushless DC motors used in aerospace and industry relies on motor fault classification and optimisation of efficiency. Accurate problem detection and diagnosis are critical for preserving system stability and performance, while attaining entirely fault-free devices is impossible according to dependability theory. This research proposes a state-of-the-art hybrid framework for motor fault classification that makes use of mutual information from current signals to effectively extract features. The representation is built on top of statistical characteristics, and to uncover hidden patterns in the data, deep features are retrieved using an adaptively trained DNN employing t-SNE visualisation. Afterwards, the Extreme Gradient Boosting (XGBoost) technique is used to integrate and classify these features. Particle Swarm Optimisation (PSO) is then used to automatically tweak the model parameters and improve performance. The results show that the suggested PSO-XGB-DNN model improves diagnostic accuracy by surpassing traditional methods, with a high classification accuracy of 97.15 percent. Finally, motor fault categorisation is made much more efficient, reliable, and operationally efficient by combining statistical and deep learning algorithms. This also improves predictive maintenance capabilities.
These findings confirm that careful data engineering is as important as model complexity and is key to achieving efficient ITSC fault diagnosis and are confirmed that careful data engineering is as important as model complexity and is key to achieving efficient ITSC fault diagnosis.
Omar Abdelaziz Bengharbi, Karim Beddek, Ahmed Yacine Lacheheb et al.· Measurement and control (Lon...· 0 citations
An enhanced diagnostic approach integrating the Sparrow Search Algorithm with PNN is developed, which confirms that SSA can substantially strengthen the anti-disturbance capability of PNN, thereby providing reliable technical support for power transformer fault diagnosis and offering certain reference value for enhancing the operational reliability of power grid equipment.
Bo Liu, Jianghong Dong, Shuyu Ren et al.· Journal of Physics, Conferen...· 0 citations
Aiming at the problems of wind turbine gearbox vibration signals with multi-frequency characteristics, difficulties in fault feature extraction and insufficient generalisation ability of traditional diagnostic models. In this study, a gearbox fault diagnosis method is proposed, which integrates variational mode decomposition (VMD) optimised by the subtraction-average-based optimiser (SABO) with a classification framework combining convolutional neural network (CNN) and support vector machine (SVM). First, the SABO algorithm is introduced to optimise the key parameters of VMD (modal number
k
and penalty factor
α
), which overcomes the limitations of traditional empirical selection and simple optimisation algorithms. Second, CNN and SVM are fused to construct an end-to-end integrated diagnostic model, using CNN to automatically extract fault features in the intrinsic modal functions (IMFs) obtained from VMD decomposition, avoiding the tediousness and subjectivity of feature selection by manual and traditional methods, and then inputting these features into SVM for classification. Finally, using the gearbox data set of Southeast University, five fault types are diagnosed and classified by MATLAB simulation experiment platform. The results demonstrate that the model constructed in this paper achieves a diagnostic accuracy of 96.43%, significantly outperforming multiple mainstream comparative models. It exhibits excellent robustness and adaptability under both noisy interference and variable operating conditions, while maintaining high computational efficiency. This provides a reliable technical solution for intelligent fault diagnosis and predictive maintenance of wind turbine gearboxes.
Minan Tang, Zhihao Fan, Jinping Li et al.· Transactions of the Institut...· 0 citations
Battery management systems (BMSs) in electric vehicles (EVs) are instrumented with an increasing number of heterogeneous sensors, many of which contribute redundant or noisy measurements that increase computational cost without improving diagnostic accuracy. This paper proposes a Binary Hybrid Particle Whale Optimization Algorithm (BHPWOA) for multi-objective feature selection targeting three-class BMS fault diagnosis: OK, Warning, and Critical. The method is evaluated using an 18-feature EV charging dataset with n=500 samples. BHPWOA encodes candidate feature subsets as binary masks in a continuous [0,1] position space. It executes a Binary Particle Swarm Optimization (BPSO) phase during the first 50 iterations to rapidly identify a promising subset region, then transfers the global-best mask as the Whale Optimization Algorithm (WOA) leader for the remaining 50 iterations of bubble-net exploitation. A multi-objective fitness function simultaneously penalises classifier error and subset size, directly optimising the accuracy–cost trade-off. BHPWOA selects four features out of 18, corresponding to a 77.8% reduction, and achieves accuracy =0.710 and macro-F1 =0.4455 on the held-out test set. It outperforms all-feature KNN F10.2997, standalone BPSO with six selected features F10.4603, BWOA with two selected features F10.4026, and BSFSA with five selected features F10.4216 on the Pareto-dominant combined fitness objective. The selected subset CellVoltageVChargeCurrentASOC%ChargePowerkW achieves the best fitness score of −0.5555, enabling a 77.8% sensor-cost reduction while improving fault detection. Stability analysis across five independent random seeds confirms a mean feature count of 4.0±0.7 and a mean macro-F1 of 0.441±0.021, demonstrating algorithmic robustness.
Buasa Andy Mayingi, B. Thango, D. Okojie· World Electric Vehicle Journ...· 0 citations
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.
Nimesh Jayasena, Battur Batkhishig, B. Nahid-Mobarakeh et al.· IEEE Open Journal of Industr...· 0 citations
Experimental results indicate that XGBoost achieves the highest accuracy in identifying unbalance, outperforming the neural network and the Bayesian model and for misalignment detection, however, the three methods exhibit comparable performance, underscoring the limitations of ML models that rely solely on vibration indicators for this fault type.
A. Marzougui, A. Hachem, T. Mazoyer· Insight - Non-Destructive Te...· 0 citations
