A Directed Acyclic Graph-Large Margin Distribution Machine Model for Music Symbol Classification.

Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs)

Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units

An advanced deep learning-based method that employs transformer architecture is proposed to diagnose the simultaneous faults with time-series data. This method can be directly applied to transient data while maintaining the accuracy without a steady-state detector so that the fault can be diagnosed in its early stage. The transformer architecture adopts a novel multi-head attention mechanism without involving any convolutional and recurrent layers as in conventional deep learning methods. The model has been verified by an on-site air handling unit with 6 single-fault cases, 7 simultaneous-fault cases, and normal operating conditions with satisfactory performances of test accuracy of 99.87%, Jaccard score of 99.94%, and F1 score of 99.95%. Besides, the attention distribution reveals the correlations between features to the corresponding fault. It is found that the length of the sliding window is key to the model performance, and a trade-off is made for the window length between the model performance and the diagnosis time. Based on the similar idea, another sequence-to-vector model based on the gated recurrent unit (GRU) is proposed and benchmarked with the transformer model. The results show that the transformer model outperforms the GRU model with a better Jaccard score and F1 score in less training time

High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD

Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve early and accurate battery system fault detection to realize rapid early warning. The method first adopts the support vector data description model mapping the feature of unlabeled voltage and temperature into a minimum volume hypersphere in high-dimensional space. When the feature is located outside the hypersphere, it is judged to be faulty. Then, to overcome the problem of hyperparameters selection, Bayesian optimization and a small amount of label data are used to iteratively train the model. This step can greatly improve the fault detection ability of the model, which is conducive to mining early and minor faults. Finally, the proposed model is compared with three unsupervised fault detection models, principal component analysis, kernel principal component analysis, and support vector data description to validate the performance of fault detection and robustness, respectively. The experimental results show that: 1. the proposed model has high detection accuracy in all four fault datasets, especially in the highly concealed cumulative short-circuit fault, which is substantially ahead of the other three models; and 2. The proposed model has higher and more stable accuracy than the other three models even in the case of a large range of signal-to-noise ratio

High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD

Fault detection of the electric vehicle battery system is vital for safe driving, energy economy, and lifetime extension. This paper proposes a data-driven method to achieve early and accurate battery system fault detection to realize rapid early warning. The method first adopts the support vector data description model mapping the feature of unlabeled voltage and temperature into a minimum volume hypersphere in high-dimensional space. When the feature is located outside the hypersphere, it is judged to be faulty. Then, to overcome the problem of hyperparameters selection, Bayesian optimization and a small amount of label data are used to iteratively train the model. This step can greatly improve the fault detection ability of the model, which is conducive to mining early and minor faults. Finally, the proposed model is compared with three unsupervised fault detection models, principal component analysis, kernel principal component analysis, and support vector data description to validate the performance of fault detection and robustness, respectively. The experimental results show that: 1. the proposed model has high detection accuracy in all four fault datasets, especially in the highly concealed cumulative short-circuit fault, which is substantially ahead of the other three models; and 2. The proposed model has higher and more stable accuracy than the other three models even in the case of a large range of signal-to-noise ratio

A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network

Developing efficient fault detection and diagnosis (FDD) techniques for building HVAC systems is important for improving buildings’ reliability and energy efficiency. The existing FDD methods can achieve satisfying results only if there are sufficient labeled training data. However, labelling the data is often costly and laborious, and most data collected in practice are unlabeled. Most of the existing FDD methods cannot leverage the unlabeled dataset which contains much information beneficial to fault classification, and this will impede the improvement of the FDD performance. To deal with this problem, a semi-supervised FDD approach is proposed for the building HVAC system based on the modified generative adversarial network (modified GAN). The binary discriminator in the original GAN is replaced with the multiclass classifier. After the modification, both the unlabeled and labeled datasets can be utilized simultaneously: the modified GAN can learn the data distribution information present in unlabeled samples and then combine this information with the limited number of labeled data to accomplish a supervised learning task. Additionally, a novel self-training scheme is proposed for the modified GAN to correct the class imbalance in both labeled and unlabeled data. With the self-training scheme, the modified GAN can still efficiently exploit the information contained in unlabeled data to enhance the FDD performance even if the class distribution is highly imbalanced. Experimental results demonstrate the effectiveness of the proposed modified GAN-based approach and the self-training scheme

An online data-driven fault diagnosis method for air handling units by rule and convolutional neural networks

The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system's historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.Published versionThis research is supported under the RIE2020 Industry Alignment Fund — Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Surbana Jurong Pte Ltd

A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system

Air balancing is a key technology to reduce energy consumption of ventilation system and improve the quality of indoor living environment. So far, most of the existing data-driven non-iterative air balancing methods only focus on the prediction of terminal damper angle to supply appropriate airflow, but they do not pay attention to the energy-saving constraint of fan voltage and terminal damper. Therefore, their energy efficiencies are not high enough. In this paper, energy-saving constraint strategy of low fan voltage and small damper friction resistance is considered and a novel data-driven non-iterative air balancing model with energy-saving constraint strategy is proposed. The model parameters can be trained by the proposed optimization algorithm inputting acquisition data. Then, given a design airflow rate, the required fan voltage and terminal damper angle can be predicted by the trained model to achieve accurate air balancing control with high energy efficiency. The performance validation of the proposed method is executed on our experimental duct system with five terminals. Compared with the current air balancing method, the proposed method can improve energy saving potential up to 13.7%, while keeping accurate air balancing within 10% relative error standard.This work is supported by NSFC (61976005, 6177219, 61902167, 61871204), Natural Science Foundation of Anhui Province (1908085MF215, 1908085QE247), Open Research Fund of AnHui Key Laboratory of Detection Technology and Energy Saving Devices (DTESD2020A03), Open Research Fund of AnHui Polytechnic University (Xjky02201903)

Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs

This paper is concerned with the modelling and prediction of random delays in networked control systems. The stochastic distribution of the random delay in the current sampling period is assumed to be affected by the network state in the current sampling period as well as the random delay in the previous sampling period. Based on this assumption, the double-chain hidden Markov model (DCHMM) is proposed in this paper to model the delays. There are two Markov chains in this model. One is the hidden Markov chain which consists of the network states and the other is the observable Markov chain which consists of the delays. Moreover, the delays are also affected by the hidden network states, which constructs the DCHMM-based delay model. The initialization and optimization problems of the model parameters are solved by using the segmental K-mean clustering algorithm and the expectation maximization algorithm, respectively. Based on the model, the prediction of the controller-to-actuator (CA) delay in the current sampling period is obtained. The prediction can be used to design a controller to compensate the CA delay in the future research. Some comparative experiments are carried out to demonstrate the effectiveness and superiority of the proposed method

A general architecture for OMR.

<p>A general architecture for OMR.</p