Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder–Decoder with operating machine sounds. RNN Encoder–Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder–Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation.

The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser‐based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor‐based sensor or tapered element oscillating microbalance‐based sensor. However, an LLS‐based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP‐GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS‐based PM measuring sensors. We conclude that our HP‐GAN is a cutting‐edge model for anomaly detection.

Methods that enable the visual inspection of solar panels are currently in demand, as a huge number of solar panels are now being deployed as a sustainable energy source. One of the solutions for inspection automation is an end-toend deep learning framework, but this is not recommended for this problem because such a framework requires not only powerful computational resources, but also a large-scale classbalanced dataset. In this study, we present a cost-effective solar panel defect detection method. We emphasize the spatial feature of defects by utilizing an attention map that is generated by a pre-trained attention mechanism that can give attention on stroke ends, gathering, and bends. We define and extract 13 statistical features from the attention map, and then feed them into conventional machine learning model. Therefore, we no longer require energy depleting models such as end-to-end neural classifiers to discriminate between non-defective and defective panels. Five conventional machine learning models and one stateof-the-art (SOTA) deep learning model—i. e., EfficientNet—are used to generalize the experimental results. The results of the comparative experiments indicate that our approach, which includes attention mechanism recycling and statistical feature extraction, is guaranteed to provide cost-effective defect detection in general with performance that is competitive with that of recent SOTA. In future research, we expect that our approach can be adopted in other defect detection tasks such as steel or film manufacturing processes.

The electric motor failure triggers the system paralyzation for various fields such as industry or transportation. Thus, continuous management is necessary. Recently, the automated anomaly detection system is adopted for reducing human exhaustion. Moreover, for improving the cost-effectiveness and monitoring stability, edge device computing is considered on system construction. For enabling edge computing, we need to achieve high performance with a low-complex anomaly detection model, considering the constrained resource. In this paper, we empirically evaluate various anomaly detection architectures from two perspectives for designing a cost-effective model. One of the perspectives is the feature aggregation method and the other one is whether to adopt the bottleneck structure or not for constructing autoencoder. The effectiveness and efficiency are improved by adopting linear feature aggregation and non-bottleneck structured auto-encoder. By combining the above two methods, the computational cost is reduced by 2 in 10k, while losing only 1.972% of the averaged anomaly detection performance.