53 research outputs found
Multi-mode Combustion Process Monitoring on a Pulverised Fuel Combustion Test Facility based on Flame Imaging and Random Weight Network Techniques
Combustion systems need to be operated under a range of different conditions to meet fluctuating energy demands. Reliable monitoring of the combustion process is crucial for combustion control and optimisation under such variable conditions. In this paper, a monitoring method for variable combustion conditions is proposed by combining digital imaging, PCA-RWN (Principal Component Analysis and Random Weight Network) techniques. Based on flame images acquired using a digital imaging system, the mean intensity values of RGB (Red, Green, and Blue) image components and texture descriptors computed based on the grey-level co-occurrence matrix are used as the colour and texture features of flame images. These features are treated as the input variables of the proposed PCA-RWN model for multi-mode process monitoring. In the proposed model, the PCA is used to extract the principal component features of input vectors. By establishing the RWN model for an appropriate principal component subspace, the computing load of recognising combustion operation conditions is significantly reduced. In addition, Hotellingâs T2 and SPE (Squared Prediction Error) statistics of the corresponding operation conditions are calculated to identify the abnormalities of the combustion. The proposed approach is evaluated using flame image datasets obtained on a 250 kWth air- and oxy-fuel Combustion Test Facility. Variable operation conditions were achieved by changing the primary air and SA/TA (Secondary Air to Territory Air) splits. The results demonstrate that, for the operation conditions examined, the condition recognition success rate of the proposed PCA-RWN model is over 91%, which outperforms other machine learning classifiers with a reduced training time. The results also show that the abnormal conditions exhibit different oscillation frequencies from the normal conditions, and the T2 and SPE statistics are capable of detecting such abnormalities
Influence of dust on temperature measurement using infrared thermal imager
Temperature measurement by infrared thermal imager is an attractive technique in many fields, and it is of great importance to ensure the measurement accuracy of the infrared thermal imager. Aiming at the influence of dust on the temperature measurement of infrared thermal imager, this paper summarized the dust influence into three categories: dust on the surface of the measured object, dust on the infrared thermal imagerâs lens and dust in the optical path between the measured object and the infrared thermal imager, and conducted three dust experiments. To quantify the measurement errors caused by dust, the infrared thermal image features that are affected by dust are extracted and a compensation model is established based on polynomial regression. The results indicate that dust can introduce measurement errors of infrared thermal imager and the proposed compensation method can compensate for the measurement errors caused by dust and improve the accuracy of infrared thermal imager
Multimode Monitoring of Oxy-gas Combustion through Flame Imaging, Principal Component Analysis and Kernel Support Vector Machine
This paper presents a method for the multimode monitoring of combustion stability under different oxy-gas fired conditions based on flame imaging, principal component analysis and kernel support vector machine (PCA-KSVM) techniques. The images of oxy-gas flames are segmented into premixed and diffused regions through Watershed Transform method. The weighted color and texture features of the diffused and premixed regions are extracted and projected into two subspaces using the PCA to reduce the data dimensions and noises. The multi-class KSVM model is finally built based on the flame features in the principal component subspace to identify the operation condition. Two classic multivariate statistic indices, i.e. Hotellingâs T2 and squared prediction error (SPE), are used to assess the normal and abnormal states for the corresponding operation condition. The experimental results obtained on a lab-scale oxy-gas rig show that the weighted color and texture features of the defined diffused and premixed regions are effective for detecting the combustion state and that the proposed PCA-KSVM model is feasible and effective to monitor a combustion process under variable operation conditions
Learning from distributed data sources using random vector functional-link networks
One of the main characteristics in many real-world big data scenarios is their distributed nature. In a machine learning context, distributed data, together with the requirements of preserving privacy and scaling up to large networks, brings the challenge of designing fully decentralized training protocols. In this paper, we explore the problem of distributed learning when the features of every pattern are available throughout multiple agents (as is happening, for example, in a distributed database scenario). We propose an algorithm for a particular class of neural networks, known as Random Vector Functional-Link (RVFL), which is based on the Alternating Direction Method of Multipliers optimization algorithm. The proposed algorithm allows to learn an RVFL network from multiple distributed data sources, while restricting communication to the unique operation of computing a distributed average. Our experimental simulations show that the algorithm is able to achieve a generalization accuracy comparable to a fully centralized solution, while at the same time being extremely efficient
Prediction of combustion state through a semi-supervised learning model and flame imaging
Accurate prediction of combustion state is crucial for an in-depth understanding of furnace performance and optimize operation conditions. Traditional data-driven approaches such as artificial neural networks and support vector machine incorporate distinct features which require prior knowledge for feature extraction and suffers poor generalization for unseen combustion states. Therefore, it is necessary to develop an advanced and accurate prediction model to resolve these limitations. This study presents a novel semi-supervised learning model integrating denoising autoencoder (DAE), generative adversarial network (GAN) and Gaussian process classifier (GPC). The DAE network is established to extract representative features of flame images and the network trained through the adversarial learning mechanism of the GAN. Structural similarity (SSIM) metric is introduced as a novel loss function to improve the feature learning ability of the DAE network. The extracted features are then fed into the GPC to predict the seen and unseen combustion states. The effectiveness of the proposed semi-supervised learning model, i.e., DAE-GAN-GPC was evaluated through 4.2ĂÂ MW heavy oil-fired boiler furnace flame images captured under different combustion states. The averaged prediction accuracy of 99.83% was achieved for the seen combustion states. The new states (unseen) were predicted accurately through the proposed model by fine-tuning of GPC without retraining the DAE-GAN and averaged prediction accuracy of 98.36% was achieved for the unseen states. A comparative study was also carried out with other deep neural networks and classifiers. Results suggested that the proposed model provides better prediction accuracy and robustness capability compared to other traditional prediction models
A hybrid noise suppression filter for accuracy enhancement of commercial speech recognizers in varying noisy conditions
Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a limitation in common in that recognition errors are likely to be produced when background noise surrounds the spoken command, thereby creating potential dangers for the disabled if recognition errors exist in the control systems. In this paper, a hybrid noise suppression filter is proposed to inter-face with the commercial speech recognizers in order to enhance the recognition accuracy under variant noisy conditions. It intends to decrease the recognition errors when the commercial speech recognizers are working under a noisy environment. It is based on a sigmoid function which can effectively enhance noisy speech using simple computational operations, while a robust estimator based on an adaptive-network-based fuzzy inference system is used to determine the appropriate operational parameters for the sigmoid function in order to produce effective speech enhancement under variant noisy conditions.The proposed hybrid noise suppression filter has the following advantages for commercial speech recognizers: (i) it is not possible to tune the inbuilt parameters on the commercial speech recognizers in order to obtain better accuracy; (ii) existing noise suppression filters are too complicated to be implemented for real-time speech recognition; and (iii) existing sigmoid function based filters can operate only in a single-noisy condition, but not under varying noisy conditions. The performance of the hybrid noise suppression filter was evaluated by interfacing it with a commercial speech recognizer, commonly used in electronic products. Experimental results show that improvement in terms of recognition accuracy and computational time can be achieved by the hybrid noise suppression filter when the commercial recognizer is working under various noisy environments in factories
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Modelling and design of the eco-system of causality for real-time systems
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.The purpose of this research work is to propose an improved method for real-time sensitivity analysis (SA) applicable to large-scale complex systems. Borrowed from the EventTracker principle of the interrelation of causal events, it deploys the Rank Order Clustering (ROC) method to automatically group every relevant system input to parameters that represent the system state (i.e. output). The fundamental principle of event modelling is that the state of a given system is a function of every acquirable piece of knowledge or data (input) of events that occur within the system and its wider operational environment unless proven otherwise. It therefore strives to build the theoretical and practical foundation for the engineering of input data. The event modelling platform proposed attempts to filter unwanted data, and more importantly, include information that was thought to be irrelevant at the outset of the design process. The underpinning logic of the proposed Event Clustering technique (EventiC) is to build causal relationship between the events that trigger the inputs and outputs of the system. EventiC groups inputs with relevant corresponding outputs and measures the impact of each input variable on the output variables in short spans of time (relative real-time). It is believed that this grouping of relevant input-output event data by order of its importance in real-time is the key contribution to knowledge in this subject area. Our motivation is that components of current complex and organised systems are capable of generating and sharing information within their network of interrelated devices and systems. In addition to being an intelligent recorder of events, EventiC could also be a platform for preliminary data and knowledge construction. This improvement in the quality, and at times the quantity of input data, may lead to improved higher level mathematical formalism. It is hoped that better models will translate into superior controls and decision making. It is therefore believed that the projected outcome of this research work can be used to predict, stabilize (control), and optimize (operational research) the work of complex systems in the shortest possible time. For proof of concept, EventiC was designed using the MATLAB package and implemented using real-time data from the monitoring and control system of a typical cement manufacturing plant. The purpose for this deployment was to test and validate the concept, and to demonstrate whether the clusters of input data and their levels of importance against system performance indicators could be approved by industry experts. EventiC was used as an input variable selection tool for improving the existing fuzzy controller of the plant. Finally, EventiC was compared with its predecessor EventTracker using the same case study. The results revealed improvements in both computational efficiency and the quality of input variable selection
Green Technologies for Production Processes
This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies
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