Abstract

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.

Highlights

  • Miguel Delgado-Prieto, Batch processing is extensively utilized in modern production fields such as food, materials, chemicals, and pharmaceuticals [1]

  • The local adaptive standardization (LAS)-Qualitative trend analysis (QTA) fault diagnosis program was completed according to the steps of Section 2.3

  • The data-based fault diagnosis method makes it difficult to mine the data information with a high mechanism, which can deviate from reality

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Summary

Introduction

Miguel Delgado-Prieto, Batch processing is extensively utilized in modern production fields such as food, materials, chemicals, and pharmaceuticals [1]. The features between batch data make processes difficult to control, presenting multi-stage characteristics in the time dimension, and a strong correlation in the variable dimension [2]. Introducing fault diagnosis technology into the batch process can effectively guarantee personnel safety and reduce economic loss. Different batches of data differ at the same time due to subtle differences in their environment, human operations, and initial conditions. Noise often covers weak fault information in the early stage of the fault, leading to delayed detection and misdiagnosis problems [3]. The research of early fault diagnosis technology in batch processes is crucial for the safe operation of the chemical plant

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