{'fraction_non_alphanumeric': 0.02570093457943925, 'fraction_numerical': 0.0035046728971962616, 'mean_word_length': 5.255474452554744, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Intelligent fault isolation of nonlinear processes with expert guidance

In this article, a semi-supervised learning model with local and global regularization is built for process monitoring. In current approaches, models are built based on historical data without expert guidance. The main contributions are as follows: (1) a new intelligent learning method for historical data with expert guidance is proposed. (2) A new similarity measurement for data with high dimensions is proposed. (3) Fault isolation approach is proposed based on the intelligent learning method. Fault isolation is considered as classification problem. In this article, the fault is isolated according to the extracted fault feature. The proposed method is applied to a robotic-arm-based spray marking system. The simulation results show the effectiveness of the proposed method.