The reliable operation of industrial systems requires not only the prompt detection of faults but also their accurate classification into the appropriate categories. At present, numerous data-driven industrial fault detection and diagnosis models, which have been developed based on historical fault data, frequently neglect the issue of label noise. When labels are corrupted by noise, a significant degradation in the performance of industrial fault detection models can be observed. In this article, alabel-noise-resistanttime-seriesclassification (LNRTSC) method based on consistency-driven label correction is proposed. First, an attention-based temporal correlation-enhanced encoder is introduced to extract low-dimensional representations of industrial time series. Then, label confidence, which is assessed based on local label consistency, is utilized to correct noisy labels during training. In addition, a two-stage self-supervised enhancement strategy is designed to guarantee the reliability of the corrected labels. Specifically, a reconstruction loss term is introduced to assist feature extraction in the warming-up stage, and a newly designed contrastive loss term is added to the loss function for the LNL training stage, which mitigates the effect of false negatives. Finally, the effectiveness of the LNRTSC method is validated on the Tennessee Eastman process and the SEU-gearbox datasets. When compared to peer methods, the LNRTSC approach demonstrates substantial improvements in fault classification performance on corrupted data.

This paper proposes a YOLO-based efficient lightweight network (YOLO-ELWNet) for onboard object detection based on the YOLOv3. A channel split and shuffle with coordinate attention module is developed in the backbone block, which effectively reduces the size of model parameters and computational cost while maintaining the detection accuracy. A new feature fusion network is proposed in the neck block, where a cross-stage partial with efficient bottleneck module is put forward to improve the feature extraction ability and reduce the computational cost. The Scylla intersection over union-based loss function is utilized in the head block, which accelerates the convergence speed of the YOLO-ELWNet. The effectiveness of the proposed YOLO-ELWNet is validated on the open source KITTI vision benchmark. The performance of YOLO-ELWNet is superior to some mainstream lightweight object detection models in terms of detection accuracy and computational cost, which demonstrates its applicability for resource-constrained onboard object detection.

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data.Note to Practitioners—Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

Near-infrared (NIR) spectroscopy plays a critical role in online analysis of difficult-to-measure properties of petrochemicals. In industrial applications, a calibration model among NIR spectra and properties must be established. However, it is a challenge to obtain a precision NIR model in the majority of petrochemical processes since industrial data present strong non-Gaussian and uncertainty characteristics. To deal with these problems, in the present work a probabilistic regression modeling method based on a biased minimax probability machine (BMPM) is proposed, without assuming any specific distributions for the data, in this article. In addition, a multiple locally weighted updating approach with a new supervised similarity distance is introduced to cope with process changes. The greatest advantage of the proposed approach is that it has superior capability in dealing with uncertainties and variations. The effectiveness of the method is illustrated through its application in an actual gasoline blending process and a simulated fermentation process.