{'fraction_non_alphanumeric': 0.014195583596214511, 'fraction_numerical': 0.0, 'mean_word_length': 5.678947368421053, '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}}
Incipient Fault Detection for Nonlinear Industrial Processes Based on Distributed Weighted Stacked Autoencoder

Incipient faults in industrial processes can deteriorate into abnormal events or even severe accidents. It is still a challenge to detect them accurately due to their slight fault symptoms. In this paper, a distributed weighted stacked autoencoder (DWSAE)-based fault detection scheme is proposed to detect incipient faults by fully utilizing the local and global fault information. First, the local stacked autoencoder models are established for each operation unit, and the feature and residual spaces are obtained. The sliding window technique and singular value decomposition (SVD) are applied to the feature spaces and residual spaces to obtain local fault information indexes. The feature vectors and residual vectors are weighted by the fault information indexes and two local detection statistics in each operation unit are constructed. Second, the global stacked autoencoder model is established using all features extracted by the local stacked autoencoders and two global detection statistics are constructed. Finally, the effectiveness and practicability of the proposed method are verified by a numerical example and Tennessee Eastman process.