Electrical motors are widely used in industrial and emerging applications such as electrical automotive. Industrial 4.0 has led to the usage of neural networks for electrical motor tasks like fault detection, monitoring, and control of electrical motors. The growing increase of neural networks in safety-critical systems requires an in-depth analysis of their robustness and stability. This paper studies the robustness of neural networks used in time-series tasks like system modeling, signal denoising, speed-torque estimation, temperature estimation, and fault detection. The dataset collected for these problems has all types of noise from the operating environment, sensors, and the system itself. This affects the performance of different network architectures during training and inference. We train and analyze under perturbations several different architectures that range from simple linear, convolutional and sequential networks to complex networks like 1D ResNet and Transformers.