Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network

Ball mills play a critical role in modern mining operations, making their bearing failures a significant concern due to the potential loss of production efficiency and economic consequences. This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. DCAN includes the modules of convolutional feature extraction and transposed convolutional feature reconstruction, demonstrating exceptional capabilities in signal processing and feature extraction. Additionally, the paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset. Experimental results validate the DCAN model's reliability in recognizing fault vibration patterns. This method holds promise for enhancing bearing fault detection efficiency, reducing production interruptions, and lowering maintenance costs.Comment: 9 pages, 11 figure

Feature-based Transferable Disruption Prediction for future tokamaks using domain adaptation

The high acquisition cost and the significant demand for disruptive discharges for data-driven disruption prediction models in future tokamaks pose an inherent contradiction in disruption prediction research. In this paper, we demonstrated a novel approach to predict disruption in a future tokamak only using a few discharges based on a domain adaptation algorithm called CORAL. It is the first attempt at applying domain adaptation in the disruption prediction task. In this paper, this disruption prediction approach aligns a few data from the future tokamak (target domain) and a large amount of data from the existing tokamak (source domain) to train a machine learning model in the existing tokamak. To simulate the existing and future tokamak case, we selected J-TEXT as the existing tokamak and EAST as the future tokamak. To simulate the lack of disruptive data in future tokamak, we only selected 100 non-disruptive discharges and 10 disruptive discharges from EAST as the target domain training data. We have improved CORAL to make it more suitable for the disruption prediction task, called supervised CORAL. Compared to the model trained by mixing data from the two tokamaks, the supervised CORAL model can enhance the disruption prediction performance for future tokamaks (AUC value from 0.764 to 0.890). Through interpretable analysis, we discovered that using the supervised CORAL enables the transformation of data distribution to be more similar to future tokamak. An assessment method for evaluating whether a model has learned a trend of similar features is designed based on SHAP analysis. It demonstrates that the supervised CORAL model exhibits more similarities to the model trained on large data sizes of EAST. FTDP provides a light, interpretable, and few-data-required way by aligning features to predict disruption using small data sizes from the future tokamak.Comment: 15 pages, 9 figure

Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection

The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.Comment: 21 pages, 11 figure

Charged Particle (Negative Ion)-Based Cloud Seeding and Rain Enhancement Trial Design and Implementation

China has been suffering from water shortage for a long time. Weather modification and rainfall enhancement via cloud seeding has been proved to be effective to alleviate the problem. Current cloud seeding methods mostly rely on solid carbon dioxide and chemicals such as silver iodide and hygroscopic salts, which may have negative impacts on the environment and are expensive to operate. Lab experiments have proved the efficiency of ion-based cloud seeding compared with traditional methods. Moreover, it is also more environmentally friendly and more economical to operate at a large scale. Thus, it is necessary to carry out a field experiment to further investigate the characteristics and feasibility of the method. This paper provides the design and implementation of the ion-based cloud seeding and rain enhancement trial currently running in Northwest China. It introduces the basic principle of the trial and the devices developed for it, as well as the installation of the bases and the evaluation method design for the trial

Determination of the number of ψ(3686)\psi(3686) events at BESIII

The numbers of ψ(3686) events accumulated by the BESIII detector for the data taken during 2009 and 2012 are determined to be and , respectively, by counting inclusive hadronic events, where the uncertainties are systematic and the statistical uncertainties are negligible. The number of events for the sample taken in 2009 is consistent with that of the previous measurement. The total number of ψ(3686) events for the two data taking periods is