Sensor drift caused by the aging components and unsuspected environmental factors is an urgent problem to be solved, as it seriously affects the detection performance and service life of electronic nose (E-nose). Existing researches mainly resorted to offline compensation techniques. Nevertheless, due to the dynamic and uncertainty of sensor drift, the offline techniques are not suitable for practical application scenarios. For this reason, the methods of compensating sensor drift online have been attracting more and more attention. To achieve the online compensation, three problems about the prediction model updating should be addressed first: ① When to update the prediction model (When); ② Which samples are used to update the prediction model (Which); ③ How to update the prediction model (How), that is, a WWH-problem. For addressing the three problems, a WWH problem-based semi-supervised online (WWH-SSO) method is proposed in this paper. The proposed WWH-SSO uses the unlabeled samples collected in the work process of E-nose to update the prediction model for realizing the unsupervised and online drift compensation. The sensor drift benchmark dataset collected by A. Vergara is used to verify the effectiveness of proposed method. The experiment results show that the sensor drift can be satisfactorily compensated online.

元器件老化和意外环境因素引起的传感器漂移是一个亟待解决的问题，严重影响电子鼻（E-nose）的检测性能和使用寿命。现有研究主要采用离线补偿技术。然而，由于传感器漂移的动态性和不确定性，离线技术不适合实际应用场景。为此，在线补偿传感器漂移的方法越来越受到关注。要实现在线补偿，首先要解决预测模型更新的三个问题：①何时更新预测模型（When）；② 使用哪些样本更新预测模型（Which）；③如何更新预测模型（How），即一个WWH-problem。针对这三个问题，本文提出了一种基于WWH问题的半监督在线（WWH-SSO）方法。提出的WWH-SSO利用E-nose工作过程中收集的未标记样本更新预测模型，实现无监督在线漂移补偿。A. Vergara 收集的传感器漂移基准数据集用于验证所提出方法的有效性。实验结果表明，传感器漂移可以得到满意的在线补偿。Vergara 用于验证所提出方法的有效性。实验结果表明，传感器漂移可以得到满意的在线补偿。Vergara 用于验证所提出方法的有效性。实验结果表明，传感器漂移可以得到满意的在线补偿。