Timely and accurate detection of abnormal working conditions can ensure stability, improve production efficiency and reduce pollution of an industrial process. However, the production data of an industrial process has non-Gaussian and time-varying characteristics due to the diverse feed composition and complex reaction mechanisms. To address the above issue, an improved online principal component analysis (PCA) algorithm based on the selective model update is proposed in this study. First, considering the non-Gaussian nature of the process data, a local outlier factor-based (LOF) abnormality detection logic is used to replace the T2 and squared prediction error (SPE) statistics in traditional PCA algorithms. Then, to adapt to the time-varying characteristics of the process data, an approximate linear dependence (ALD) algorithm is used to evaluate the independent degree between the new sample and training samples. Only those samples containing new information are used to update the monitoring model, which can improve model performance and reduce the frequency of online updates. The zinc roasting process (ZRP) is used as an example to illustrate the proposed approach. Industrial data collected from a ZRP is used to demonstrate the performance of the ALD-based LOF-PCA method in the early detection of two typical abnormal working conditions in the ZRP.