An Intelligent Fault Detection Method Based on Sparse Auto-Encoder for Industrial Process Systems: A Case Study on Tennessee Eastman Process Chemical System

Ren, Hao; Chai, Yi; Qu, Jianfeng; Zhang, Ke; Tang, Qiu

doi:10.1109/ihmsc.2018.00051

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Other examples of the use of autoencoders relate to testing the quality of electrode coating in the automotive industry [ 39 ], testing the quality of sheet metal using data obtained directly from production [ 40 ], controlling the printed circuit board using data from production [ 41 ] and on artificial data [ 42 ]. In addition, there are numerous other examples of using autoencoders to test quality in production tasks [ 43 , 44 , 45 ].…”

Section: Related Workmentioning

confidence: 99%

Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications

Kocon,

Malesa,

Rapcewicz

2023

Sensors

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Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.

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