Energy load forecasting across multiple buildings is beneficial for energy saving. Currently, most methodologies are training a single global model for all buildings as the deep learning model relies on large-scale data. However, the energy data distribution may vary a lot across different buildings and enforcing a global model may cause unnecessary computing resource overutilization. Meanwhile, building energy management encounters repeated manual efforts for machine learning model training over the new sensor data. To improve the computing resource utilization of load forecasting model training and automation of building energy management, a new automatic learning framework is proposed to support automatic building energy data analytics. The machine learning model is customized for each building based on an automatic algorithm with efficient model evaluations. The new framework brings comparable performance to federated energy data learning while fewer computing resource is consumed.
Pipeline systems are critical infrastructure for modern economies, which serve as the essential means for transporting oil, gas, water, and other fluids. These pipelines are mostly buried underground, making their integrity highly crucial. Because they are buried, these pipelines are subject to stress and are prone to material degradation due to corrosion. Corrosion not only reduces the wall thickness of the pipes but also poses severe safety risks and can lead to catastrophic failures and substantial financial losses. Hence, there is an urgent need to develop accurate predictive models for evaluating pipe wall thickness. This paper aims to address this need by exploring machine learning-based algorithms to monitor the corrosion rates so that preventive measures can be taken to ensure pipeline integrity. Thus, four state-of-the-art machine-learning algorithms, namely, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM) are employed to predict accurate wall thickness of pipelines. The empirical results show that the LSTM algorithm outperforms its counterparts, achieving a low root mean squared error (RMSE) of 0.0721 mm. Therefore, incorporating LSTM-based models into pipeline integrity programs can be a significant step forward to safeguard these critical infrastructures.
KEYWORDS: Corrosion, Inspection, 3D modeling, Deep learning, Magnetism, Image processing, Metals, 3D image reconstruction, Signal processing, Machine learning
Magnetic flux leakage (MFL) is a widely used nondestructive testing technique in pipeline inspection to detect and quantify defects. In pipeline integrity management, the reconstruction of defects from MFL signals plays a critical role in failure pressure prediction and maintenance decision-making. In current research practices, this reconstruction primarily involves the determination of defect dimensions, including length, width, and depth, collectively forming a rectangular box. However, this box-based representation potentially leads to conservative assessments of pipeline integrity. To fine-scale the reconstruction results and provide detailed defect information for the integrity assessment, a 3-D reconstruction model for pipeline corrosion defects from MFL signals is proposed. In detail, the deep neural network is established to capture the nonlinear relationship between the MFL signals and 3-D defect profiles. In contrast to the limited insights offered by the box profile, the reconstructed 3-D profile in this paper enables more detailed metal loss geometry. The experiments using field pipeline in-line inspection data demonstrate promising results on both morphology and depth prediction.
KEYWORDS: Deep learning, Data modeling, Transformers, Education and training, Machine learning, Neural networks, Performance modeling, Visualization, Data privacy, Autoregressive models
Building energy consumption grows rapidly with modern urbanization while the buildings’ sensor data also increases explosively. Improving energy utilization of community buildings is critical for sustainable development and global climate challenge. However, the data isolation across buildings’ privacy management prevents largescale machine learning model training, which may reduce the prediction accuracy due to lack of data. Federated building energy learning supports distributed learning through model sharing so that data privacy is mitigated. In federated learning, model-sharing brings a new concern about network resource limitation. Deep learning model transfers across multiple buildings would cause network ingestion and incur high latency of federated training. To improve the efficiency of federated training with fewer resources, a new federated learning algorithm is proposed with a new deep learning model design. The deep learning model memory usage is reduced by 80% while energy load forecasting accuracy is still comparable to the state-of-the-art methods.
The effects of the lightning strike on composite aircraft structures have been an active research area in the aviation industry, given the concern over safe aircraft operations. To maintain safe operations, civil and military regulators require effective approaches to assess and quantify the severity of lightning damage. Although x-rays are commonly used to determine material damage in aircraft structures, the technique requires access to both sides of the investigated part. This paper proposes a novel autoencoder model to check the feasibility of evaluating the damage to carbon fiber reinforced polymers (CFRP) panels from the outer surface of in-service aircraft structures. Two alternative techniques to x-ray, such as ultrasonic testing (UT) and infrared thermography (IR), nondestructive evaluation methods, are employed to develop the proposed model. The fusion model uses U-net as the backbone and spatial attention fusion as the fusion strategy while combining structural similarity index (SSIM) and perceptual losses as the loss function. Also, the log-Gabor filter is used in the model to obtain high-frequency edge information for fusion. The results are then compared against five state-of-the-art fusion methods, revealing that the proposed model performs better in quantifying the lightning damage to aircraft CFRP structures.
Condition assessment of underground buried utilities, especially water distribution networks, is crucial to the decision making process for pipe replacement and rehabilitation. Hence, regular inspection of the water pipelines is carried out with in-pipe inspection robots to assess the internal condition of the water pipelines. However, the inspection robots need to identify and negotiate with the valves to pass through. Therefore, the aim of this study is to detect the valves in water pipelines in real-time to ensure smooth operation of the inspection robot. In this paper, four state-of-the-art deep neural network algorithms namely, Faster R-CNN, RFCN, SSD, and YOLO are presented to perform the real-time valve detection analysis. The study shows that Faster R-CNN, pre-trained with Resnet101 outperforms all the selected models by achieving 97:35% and 76:73% mean Average precison (mAP) values when the threshold for prediction is set to 50% and 75% respectively. However, in terms of the detection rate in frames per second (FPS), YOLOv3-608 seems to have better processing speed than all other models.
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