Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 23; p. 8075
• Main Authors: Yao, Yuman, Dai, Yiyang, Luo, Wenjia
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 02.12.2021; MDPI
• Subjects: Batch processes; Batch processing; Deep learning; Diagnostic systems; Euclidean geometry; Failure rates; Fault diagnosis; Fermentation; incipient fault detection; Knowledge; Language; Noise; Penicillin; Principal components analysis; QTA; Standard deviation; Standardization; Statistical analysis; Statistical methods; Trend analysis; Trends; Variables
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21238075

The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.