An anomaly detection method based on double encoder–decoder generative adversarial networks

Abstract

Purpose This study aims to address the challenge of training a detection model for the robot to detect the abnormal samples in the industrial environment, while abnormal patterns are very rare under this condition. Design/methodology/approach The authors propose a new model with double encoder–decoder (DED) generative adversarial networks to detect anomalies when the model is trained without any abnormal patterns. The DED approach is used to map high-dimensional input images to a low-dimensional space, through which the latent variables are obtained. Minimizing the change in the latent variables during the training process helps the model learn the data distribution. Anomaly detection is achieved by calculating the distance between two low-dimensional vectors obtained from two encoders. Findings The proposed method has better accuracy and F1 score when compared with traditional anomaly detection models. Originality/value A new architecture with a DED pipeline is designed to capture the distribution of images in the training process so that anomalous samples are accurately identified. A new weight function is introduced to control the proportion of losses in the encoding reconstruction and adversarial phases to achieve better results. An anomaly detection model is proposed to achieve superior performance against prior state-of-the-art approaches.

Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

“…Deep learning technology could help firms maintain the process and ascertain any anomaly in the datasets (Collins Jackson & Lacey, 2020 ). Traditional processes for anomaly detection, like statistical analysis, are helpful, but deep learning technology can offer more precise information for manufacturing firms to use to correct their course (Liu et al, 2020 ; Xie et al, 2020 ). Thus, ADC can be considered an important component of deep learning technology and a strategic resource that the manufacturing firm can exploit to achieve success.…”

“…The market of deep learning technology is growing rapidly, and it is forecast that the market of deep learning technology in the USA will reach $10.2 billion by 2025 (Grand View Research, 2017 ). A manufacturing firm’s deep learning capability can help it towards predictive maintenance, quality control, as well as anomaly detection (Bromuri et al, 2020 ; Liu et al, 2020 ).…”

“…Nevertheless, deep learning technology can improve a manufacturing firm’s performance provided the top management extends effective support to using it (Adebowale et al, 2020 ). Studies have demonstrated how deep learning ability of a manufacturing firm could help the firm for its quality control, predictive maintenance along with appropriate detection of anomaly (Chen et al, 2021 ; Liu et al, 2020 ). Studies are found to have documented that for effective predictive performance, deep learning technology could supersede the effectiveness of machine-learning applications in this context (Guan et al, 2019 ; Wong et al, 2021 ).…”

Guest editorial

“…The first paper (Liu et al, 2020) is to propose a novel anomaly detection to address the problem of very rare abnormal patterns when robots detect abnormal samples in the industrial environment. This method designs a double encoder-decoder generative adversarial networks to detect anomalies and uses the double encoder-decoder approach to map high-dimensional input images to a low-dimensional space.…”