Salas-Robles Jorge E, Biot-Monterde Vicente, Antonino-Daviu Jose A
Escuela Técnica Superior de Ingeniera Aeroespacial y Diseño Industrial, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
Entropy (Basel). 2024 Aug 30;26(9):744. doi: 10.3390/e26090744.
Brushed DC motors and generators (DCMs) are extensively used in various industrial applications, including the automotive industry, where they are critical for electric vehicles (EVs) due to their high torque, power, and efficiency. Despite their advantages, DCMs are prone to premature failure due to sparking between brushes and commutators, which can lead to significant economic losses. This study proposes two approaches for determining the temporal and frequency evolution of Shannon entropy in armature current and stray flux signals. One approach indirectly achieves this through prior analysis using the Short-Time Fourier Transform (STFT), while the other applies the Stockwell Transform (S-Transform) directly. Experimental results show that increased sparking activity generates significant low-frequency harmonics, which are more pronounced compared to mid and high-frequency ranges, leading to a substantial rise in system entropy. This finding enables the introduction of fault-severity indicators or Key Performance Indicators (KPIs) that relate the current condition of commutation quality to a baseline established under healthy conditions. The proposed technique can be used as a predictive maintenance tool to detect and assess sparking phenomena in DCMs, providing early warnings of component failure and performance degradation, thereby enhancing the reliability and availability of these machines.
有刷直流电动机和发电机（DCM）广泛应用于各种工业领域，包括汽车行业，在该行业中，由于其高扭矩、功率和效率，它们对电动汽车（EV）至关重要。尽管DCM具有诸多优点，但由于电刷与换向器之间的火花，它们容易过早失效，这可能导致重大的经济损失。本研究提出了两种方法来确定电枢电流和杂散磁通信号中香农熵的时间和频率演变。一种方法通过使用短时傅里叶变换（STFT）进行先验分析间接实现这一目标，而另一种方法则直接应用斯托克韦尔变换（S变换）。实验结果表明，增加的火花活动会产生显著的低频谐波，与中高频范围相比更为明显，从而导致系统熵大幅上升。这一发现使得能够引入故障严重程度指标或关键性能指标（KPI），将换向质量的当前状况与在健康条件下建立的基线相关联。所提出的技术可作为一种预测性维护工具，用于检测和评估DCM中的火花现象，提供部件故障和性能退化的早期预警，从而提高这些机器的可靠性和可用性。
