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fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence

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Energy and AI 2024年第2期16卷 316-330页

作者： Fangzhou Guo Zhijie Chen Fu Xiao Department of Building Environment and Energy Engineering Hong Kong Polytechnic UniversityHong Kong Lawrence Berkeley National Laboratory BerkeleyCAUSA Research Institute for Smart Energy Hong Kong Polytechnic UniversityHong Kong

The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.

关键词： fault detection and diagnosis Predictive maintenance Air conditioner Electric vehicle Data-driven model Gaussian process

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A modified transformer and adapter-based transfer learning for fault detection and diagnosis in HVAC systems

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Energy Storage and Saving 2024年第2期3卷 96-105页

作者： Zi-Cheng Wang Dong Li Zhan-Wei Cao Feng Gao Ming-Jia Li Key Laboratory of Thermo-Fluid Science and Engineering of Ministry of Education School of Energy and Power EngineeringXi’an Jiaotong UniversityXi’an710049China Science and Technology on Space Physics Laboratory China Academy of Launch Vehicle TechnologyBeijing100076China School of Mechanical Engineering Beijing Institute of TechnologyBeijing100081China

fault detection and diagnosis(FDD)of heating,ventilation,and air conditioning(HVAC)systems can help to improve the energy saving in building energy ***,most data-driven trained FDD models have limited generalizability and can only be applied to specific *** diversity of HVAC systems and the high cost of data acquisition present challenges for the practical application of *** learning technology can be employed to mitigate this problem by training a model on systems with sufficient data and then transfer it to other systems with limited *** this study,a novel transfer learning approach for HVAC FDD is ***,the transformer model is modified to incorporate one encoder and two decoders connected,enabling two *** modified transformer model accommodates absent features in the target domain and serves as a robust foundation for transfer *** has effective performance in complex systems and achieves an accuracy of 91.38%for a system with 16 faults and multiple fault severity ***,the adapter-based parameter-efficient transfer learning method,facilitating the transfer of trained models simply by inserting small adapter modules,is investigated as the transfer learning *** demonstrate that this adapter-based transfer learning approach achieves satisfactory performance similar to full fine-tuning with fewer trainable *** works well with limited data amount in target ***,the findings highlight the significance of adapters positioned near the bottom and top layers,emphasizing their critical role in facilitating successful transfer learning.

关键词： fault detection and diagnosis Transfer learning HVAC system Energy saving Transformer model

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fault detection and diagnosis for data incomplete industrial systems with new Bayesian network approach

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Journal of Systems Engineering and Electronics 2013年第3期24卷 500-511页

作者： Zhengdao Zhang Jinlin Zhu Feng Pan Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan University

For the fault detection and diagnosis problem in largescale industrial systems, there are two important issues： the missing data samples and the non-Gaussian property of the data. However, most of the existing data-driven methods cannot be able to handle both of them. Thus, a new Bayesian network classifier based fault detection and diagnosis method is proposed. At first, a non-imputation method is presented to handle the data incomplete samples, with the property of the proposed Bayesian network classifier, and the missing values can be marginalized in an elegant manner. Furthermore, the Gaussian mixture model is used to approximate the non-Gaussian data with a linear combination of finite Gaussian mixtures, so that the Bayesian network can process the non-Gaussian data in an effective way. Therefore, the entire fault detection and diagnosis method can deal with the high-dimensional incomplete process samples in an efficient and robust way. The diagnosis results are expressed in the manner of probability with the reliability scores. The proposed approach is evaluated with a benchmark problem called the Tennessee Eastman process. The simulation results show the effectiveness and robustness of the proposed method in fault detection and diagnosis for large-scale systems with missing measurements.

关键词： fault detection and diagnosis Bayesian network Gaussian mixture model data incomplete non-imputation.

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Rotating machinery fault detection and diagnosis based on deep domain adaptation:A survey

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Chinese Journal of Aeronautics 2023年第1期36卷 45-74页

作者： Siyu ZHANG Lei SU Jiefei GU Ke LI Lang ZHOU Michael PECHT Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology School of Mechanical EngineeringJiangnan UniversityWuxi 214122China HUST-Wuxi Research Institute Wuxi 214071China Center for Advanced Life Cycle Engineering University of MarylandCollege ParkMD 20742USA

In practical mechanical fault detection and diagnosis,it is difficult and expensive to collect enough large-scale supervised data to train deep *** learning can reuse the knowledge obtained from the source task to improve the performance of the target task,which performs well on small data and reduces the demand for high computation ***,the detection performance is significantly reduced by the direct transfer due to the domain *** adaptation(DA)can transfer the distribution information from the source domain to the target domain and solve a series of problems caused by the distribution difference of *** this survey,we review various current DA strategies combined with deep learning(DL)and analyze the principles,advantages,and disadvantages of each *** also summarize the application of DA combined with DL in the field of fault *** paper provides a summary of the research results and proposes future work based on analysis of the key technologies.

关键词： Deep learning Domain adaptation fault detection and diagnosis Transfer learning

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Deep learning technique for process fault detection and diagnosis in the presence of incomplete data

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Chinese Journal of Chemical Engineering 2020年第9期28卷 2358-2367页

作者： Cen Guo Wenkai Hu Fan Yang Dexian Huang Department of Automation Tsinghua UniversityBeijing 10084China Cornell University NY 14850United States of America University of Alberta EdmontonAB T6G 1H9Canada

In modern industrial processes, timely detection and diagnosis of process abnormalities are critical for monitoring process operations. Various fault detection and diagnosis(FDD) methods have been proposed and implemented, the performance of which, however, could be drastically influenced by the common presence of incomplete or missing data in real industrial scenarios. This paper presents a new FDD approach based on an incomplete data imputation technique for process fault recognition. It employs the modified stacked autoencoder,a deep learning structure, in the phase of incomplete data treatment, and classifies data representations rather than the imputed complete data in the phase of fault identification. A benchmark process, the Tennessee Eastman process, is employed to illustrate the effectiveness and applicability of the proposed method.

关键词： Alarm configuration Deep learning fault detection and diagnosis Incomplete data Stacked autoencoder

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Applied fault detection and diagnosis for Industrial Gas Turbine Systems

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International Journal of Automation and computing 2017年第4期14卷 463-473页

作者： Yu Zhang Chris Bingham Mike Garlick Michael Gallimore School of Engineering University of Lincoln Lincoln LN6 7TS UK Siemens Industrial Turbomachinery Lincoln LN6 3AD UK

The paper presents readily implementable approaches for fault detection and diagnosis （FDD） based on measurements from multiple sensor groups, for industrial systems. Specifically, the use of hierarchical clustering （HC） and self-organizing map neural networks （SOMNNs） are shown to provide robust and user-friendly tools for application to industrial gas turbine （IGT） systems. HC fingerprints are found for normal operation, and FDD is achieved by monitoring cluster changes occurring in the resulting dendrograms. Similarly, fingerprints of operational behaviour are also obtained using SOMNN based classification maps （CMs） that are initially determined during normal operation, and FDD is performed by detecting changes in their CMs. The proposed methods are shown to be capable of FDD from a large group of sensors that measure a variety of physical quantities. A key feature of the paper is the development of techniques to accommodate transient system operation, which can often lead to false-alarms being triggered when using traditional techniques if the monitoring algorithms are not first desensitized. Case studies showing the efficacy of the techniques for detecting sensor faults, bearing tilt pad wear and early stage pre-chamber burnout, are included. The presented techniques are now being applied operationally and monitoring IGTs in various regions of the world.

关键词： fault detection and diagnosis hierarchical clustering, self-organizing map neural network.

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A Reinforcement Learning System for fault detection and diagnosis in Mechatronic Systems

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Computer Modeling in Engineering & Sciences 2020年第9期124卷 1119-1130页

作者： Wanxin Zhang Jihong Zhu School of Electronics and Information Technology Sun Yat-sen UniversityGuangzhou510006China Department of Computer Science and Technology Tsinghua UniversityBeijing100084China

With the increasing demand for the automation of operations and processes in mechatronic systems,fault detection and diagnosis has become a major topic to guarantee the process *** exist numerous studies on the topic of applying artificial intelligence methods for fault detection and ***,much of the focus has been given on the detection of *** terms of the diagnosis of faults,on one hand,assumptions are required,which restricts the diagnosis *** the other hand,different faults with similar symptoms cannot be distinguished,especially when the model is not trained by plenty of *** this work,we proposed a reinforcement learning system for fault detection and *** assumption is *** exaction is first *** with the features as the states of the environment,the agent directly interacts with the *** policy,which determines the exact category,size and location of the fault,is obtained by updating Q *** method takes advantage of expert *** the features are unclear,action will be made to get more information from the new state for further *** create recurrent neural network with the long short-term memory architecture to approximate Q *** application on a motor is *** experimental results validate that the proposed method demonstrates a significant improvement compared with existing state-of-the-art methods of fault detection and diagnosis.

关键词： Classification reinforcement learning neural network feature exaction and selection fault detection and diagnosis

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Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis

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能源与人工智能(英文) 2023年第2期12卷 206-233页

作者： Fan Zhang Nausheen Saeed Paria Sadeghian School of Technology and Business Studies Sweden Department of Energy Technology Dalarna UniversityFalun79188Sweden

Building sector account for significant global energy consumption and Heating Ventilation and Air Conditioning (HVAC) systems contribute to the highest portion of building energy consumption. Therefore, the potential for energy saving by improving the efficiency of HVAC systems is huge and various fault detection and diagnosis (FDD) methods have been studied for this purpose. Although amongst all types of existing FDD methods, datadriven based ones are regarded as the most effective methods. As a relatively new branch of data-driven approaches, deep learning (DL) methods have shown promising results, a comprehensive review of DL applications in this area is absent. To fill the research gap, this systematic review with meta analysis analyses the relevant studies both quantitatively and qualitatively. The review is conducted by searching Web of Science, ScienceDirect, and Semantic search. There are 47 eligible studies included in this review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. 6 out of the 47 studies are identified as eligible for meta analysis of the effectiveness of DL methods for FDD. The most used DL method is 2D convolutional neural network (CNN). Results suggest that DL methods show promising results as a HVAC FDD. However, most studies use simulation/lab experiment data and real-world complexities are not fully investigated. Therefore, DL methods need to be further tested with real-world scenarios to support decision-making.

关键词： HVAC Deep learning Systematic review Meta analysis fault detection and diagnosis

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Novel Approach for fault detection and diagnosis of Sensors in Air Handling Units Using Wavelet Energy Entropy

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Journal of Donghua University(English Edition) 2013年第3期30卷 207-214页

作者：李素萍张玉华 School of Information Engineering North China University of Water Conservancy and Electric Power

A novel fault diagnosis method for sensors in air handling unit（AHU） using wavelet energy entropy was presented. Instead of directly comparing the numerous data under noise conditiom, the wavelet energy entropy residual was compared in the proposed method. *** wavelet analysis was used to decompose the measurement data under both fault-free and faulty operation conditions. The concept of Shannon entropy was referred to define wavelet energy entropy of the wavelet coefficients. The sensor faults were diagnosed by comparing the deviation of the wavelet energy entropy of the measured signal and the estimated one with the preset threshold. Testing results showed that the wavelet energy entropy was sensitive to diagnose the biased faults. The wavelet energy entropy residuals exceed the threshold significantly when faults occur. In addition, the severer the faults were, the larger the residuals would be. The results prove that the proposed method is feasible and effective for the fault detection and diagnosis of the sensors.

关键词： fault detection and diagnosis sensor air handling unit ( AHU) wavelet analysis Shannon entropy

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fault detection and diagnosis of Nonlinear Processes Based on Kernel ICA-KCCA

Fault Detection and Diagnosis of Nonlinear Processes Based o...

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2010 Chinese Control and Decision Conference

作者： Shuai Tan~1,Fuli Wang~(1,2),Yuqing Chang~(1,2),Weidong Chen~3,Jiazhuo Xu~3 1.School of Information Science and Engineering,Northeastern University,Shenyang,110004 2.Key Laboratory of Integrated Automation of Process Industry,Ministry of Education,Northeastern University,Shenyang 110004 3.Baosteel Industry Inspection Corp.,Shanghai 201900

＜正＞fault detection and diagnosis based on multivariate statistical way is a hotspot in recent years. According to the nonlinear property of Continuous Annealing Line,this article developes a nonlinear ICA, which combined the predominance of ICA and reproducing kernel Hilbert space,to monitor *** method has better statistical attribute than traditional ICA algorithm based on maximum negentropy,and it performs more robust and flexible to the variety of signal *** last,the simulation results of practical production reveal that the kernel ICA-KCCA algorithm is more effective than traditional ICA method.

关键词： Independent Component Analysis Canonical Correlation Analysis Kernel Space Nonlinear Processes fault detection and diagnosis

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