Nonlinearity modeling for online estimation of industrial cooling fan speed subject to model uncertainties and state-dependent measurement noise - Peng Chao-Chung; Tsai Min-Che; Chen Tsai-Ying (Nonlinear Engineering)
This article presents an online speed estimation method for cooling fans in resource-limited embedded systems, considering modeling uncertainties and measurement noise. In the current thriving information technology era, monitoring the state of cooling fans is crucial, particularly for high-performance artificial intelligence server cabinets. Accurate fan speed estimation can be used not only to detect fan abnormalities but also for speed control-related applications. Several challenges arise in developing speed estimation algorithms, including state-dependent measurement noise variance, errors in nonlinear fan dynamic modeling, and uncertainties in parameter estimation. To address these issues, this study employs the unscented Kalman filter (UKF) algorithm, incorporating state-dependent noise modeling and mathematical modeling of parameter uncertainties. An UKF-based parameter update mechanism is developed to compensate for model uncertainties and estimation errors, improving the speed estimation accuracy. Simulation results indicate that the root-mean-square errors are reduced from 1.3393 with the traditional UKF to 0.7485 with the parameter update mechanism. Experimental verifications further validate the effectiveness of the proposed methods and strategies in addressing the challenges associated with speed estimation in cooling fans under uncertainties and noise interference.