Integrating Blended Learning in Global Energy Education: Efficacy Evaluation & Insights from the 3D Energy Project Pilot

The rapid paradigm shifts in the global energy industry has emphasize a great need for a workforce that is well-equipped to combat its multifaceted challenges. Although blended learning models have become more common in numerous educational domains, their role in global energy education is not very well established. This paper focuses on evaluating blended learning within the context of the Decarbonisation, Decentralisation, and Digitalisation (3D) Energy Project to further our identification and understanding of a sustainable global energy education learning model. In this research, a novel learning programme involving a physical summit coupled with an online microcredential course, was introduced. This programme conducted a pilot study, which involved a group of postgraduate and final year undergraduate students from Malaysia, Vietnam, and China. Using quantitative and qualitative feedback, this paper sought to assess the summit’s efficacy in fostering international collaborations and broadening participants’ perspectives on energy challenges. Our principal findings indicate that participants greatly valued the opportunity for cross-cultural interactions, gained a deeper comprehension of energy-related topics, and found the course structure to be beneficial. Furthermore, the feedback revealed certain areas for improvement, such as additional language assistance and an extension to the physical summit for more in-depth interactions between countries. To summarise, the programme’s blended learning approach highlighted it’s potential for the future of global energy education

Novelty detection with self-organizing maps for autonomous extraction of salient tracking features

International audienceIn the image processing field, many tracking algorithms rely on prior knowledge like color, shape or even need a database of the objects to be tracked. This may be a problem for some real world applications that cannot fill those prerequisite. Based on image compression techniques, we propose to use Self-Organizing Maps to robustly detect novelty in the input video stream and to produce a saliency map which will outline unusual objects in the visual environment. This saliency map is then processed by a Dynamic Neural Field to extract a robust and continuous tracking of the position of the object. Our approach is solely based on unsupervised neural networks and does not need any prior knowledge, therefore it has a high adaptability to different inputs and a strong robustness to noisy environments

Classification of ball bearing faults using a hybrid intelligent model

In this paper, classification of ball bearing faults using vibration signals is presented. A review of condition monitoring using vibration signals with various intelligent systems is first presented. A hybrid intelligent model, FMM-RF, consisting of the Fuzzy Min-Max (FMM) neural network and the Random Forest (RF) model, is proposed. A benchmark problem is tested to evaluate the practicality of the FMM-RF model. The proposed model is then applied to a real-world dataset. In both cases, power spectrum and sample entropy features are used for classification. Results from both experiments show good accuracy achieved by the proposed FMM-RF model. In addition, a set of explanatory rules in the form of a decision tree is extracted to justify the predictions. The outcomes indicate the usefulness of FMM-RF in performing classification of ball bearing faults.Professor Nandi is a Distinguished Visiting Professor at Tongji University, Shanghai, China. This work was partly supported by the National Science Foundation of China grant number 61520106006 and the National Science Foundation of Shanghai grant number 16JC1401300

Compressive sensing strategy for classification of bearing faults

© 2017 IEEE. Owing to the importance of rolling element bearings in rotating machines, condition monitoring of rolling element bearings has been studied extensively over the past decades. However, most of the existing techniques require large storage and time for signal processing. This paper presents a new strategy based on compressive sensing for bearing faults classification that uses fewer measurements. Under this strategy, to match the compressed sensing mechanism, the compressed vibration signals are first obtained by resampling the acquired bearing vibration signals in the time domain with a random Gaussian matrix using different compressed sensing sampling rates. Then three approaches have been chosen to process these compressed data for the purpose of bearing fault classification these includes using the data directly as the input of classifier, and extract features from the data using linear feature extraction methods, namely, unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA). Classification performance using Logistic Regression Classifier (LRC) achieved high classification accuracy with significantly reduced bandwidth consumption compared with the existing techniques

A fleet-wide approach for condition monitoring of similar machines using time-series clustering

© Springer Nature Switzerland AG 2019. The application of machine learning to fault diagnosis allows automated condition monitoring of machines, leading to reduced maintenance costs and increased machine availability. Traditional approaches train a machine learning algorithm to identify specific faults or operational settings. Therefore, these approaches cannot always cope with a dynamic industrial environment. However, an industrial installation often contains multiple machines of the same type, which enables a fleet-based analysis. This type of analysis compares machines to tackle the challenges of a dynamic environment. In this paper a novel method is proposed for analyzing a fleet of machines operating under similar conditions in the same area by using inter-machine comparisons. The proposed methodology consists of two steps. First, the inter-machine difference is calculated using dynamic time warping by using the amount of warping as measure. This method allows comparing the measured signals even when fluctuations are present. Second, a clustering method uses the inter-machine similarity to identify groups of machines that operate in a similar manner. The generation of a fault usually causes a change in the raw signals and diagnostic features. As a result, the inter-machine difference between the faulty machine and the rest of the fleet will increase, leading to the creation of a separate group that contains the faulty machine. The methodology is evaluated and validated on phase current signals measured on a fleet of electrical drivetrains, where a phase unbalance fault is introduced in some of the drivetrains for a specific time duration.status: publishe

Eco-Energetical analysis of circular economy and community-based virtual power plants (CE-cVPP): A systems engineering-engaged life cycle assessment (SE-LCA) method for sustainable renewable energy development

Amidst the escalating complexity of energy demands and climate concerns, naturalcalamities, and the Black Swan phenomenon have all presented formidable obstaclesto the advancement and implementation of renewable energy sources. In light of thecurrent backlog in energy technology, this study proposes a novel renewable energydevelopment initiative—community-based virtual power plant (CE-cVPP)—thatintegrates community participation and circular economy in order to promote robustdevelopment and broaden the path to sustainable development. The principal focusesof the model are community renewable energy supply, energy demand, andcommunity participation management. The sustainable framework is constructed uponthe principles of circular economy theory. On the basis of disparities in renewableenergy sources and a variety of spatiotemporal dimensions, CE-cVPP is categorisedinto four representative groups. We present a business community model for energyand environmental analysis that is based on a hospital in Harbin. A novel approach tolife cycle assessment (SE-LCA) rooted in systems engineering is suggested as ameans of conducting a methodical and scientific evaluation of energy, environmental,and economic sustainability. The findings indicate that when compared to conventionalapproaches, the full lifecycle annual total cost saving ratio is 54.21%, the full lifecycleprimary energy saving rate is 67.21%, the full lifecycle emission reduction ratios foratmospheric pollutants are 53.50% (SO2-eq.), 51.69% (PM2.5-eq.), and 74.85% (CO2-eq.). Theoretically and methodologically, this research may be applied to the energyand power sectors