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A new identification method of high voltage circuit breaker mechanical fault based on combined analysis of time-reference-distance and shape entropy algorithms

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第17期16卷 3416-3425页

作者： Liu, Huilan Xu, Wenjie Zhao, Shutao Wang, Erxu Liu, Jiaomin Peng, Yonglong Li, Yabin North China Elect Power Univ Dept Elect Power Engn Baoding 071003 Peoples R China State Grid Corp China Baoding Power Supply Co Baoding Peoples R China

The vibration of high voltage circuit breakers is superimposed by the impact vibration generated by the action of its components. When the operating states of the components change, the vibration waveform will be distorted. To extract the state information contained in the vibration signals, in this paper we propose a new method for the mechanical fault identification of circuit breakers based on the joint analysis of time-frequency domain features. Firstly, a new time-reference-distance algorithm (TRD) which employs feature space to grab information is proposed to get the time-domain features in detail. The feature space is constructed by the kurtosis, crest factor, and peak divergence degree of the time-domain waveform. Further, we propose another new algorithm, the shape entropy algorithm (SE), to extract the frequency-domain features. Through converting the power spectrum of the vibration signals to polar coordinates via the divergence factor, the SE features are then obtained by combining with the information entropy formula. Finally, the TRD and SE features are combined and input into the support vector machine (SVM) model for fault identification. The test results verify the efficiency of the methodology and the identification process takes less time. It has a practical application value.

关键词： information entropy formula shape entropy algorithm vibration waveform support vector machines circuit breakers joint analysis feature space peak divergence degree entropy frequency-domain features time-reference-distance algorithm fault diagnosis vibrations mechanical engineering computing TRD mechanical fault identification identification method components change operating states state information time-domain waveform feature extraction high voltage circuit breaker mechanical fault time-domain features identification process vibrational signal processing SE features divergence factor high voltage circuit breakers time-frequency domain features vibration signals impact vibration

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Privacy-preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

IET COLLABORATIVE INTELLIGENT MANUFACTURING

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IET COLLABORATIVE INTELLIGENT MANUFACTURING 2022年第3期4卷 208-219页

作者： Xia, Liqiao Zheng, Pai Li, Jinjie Tang, Wangchujun Zhang, Xiangying Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong 999077 Peoples R China Hong Kong Polytech Univ Dept Comp Sci Hong Kong Peoples R China Univ Cambridge Inst Mfg Dept Engn Cambridge England Zhejiang Univ Sch Mech Engn Inst Ind Engn Hangzhou Peoples R China

Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.

关键词： collaborative bearing fault diagnosis safeguarding data privacy learning (artificial intelligence) insufficient data cryptography fault diagnosis data silos persuasive performance effective fault diagnosis models machine bearings model information vibrational signal processing privacy-preserving gradient data volume vertical FL bearing fault case studies Autoencoder model manufacturing scenarios data privacy mechanical engineering computing collaborative fault diagnosis data-driven fault diagnosis approaches privacy-preserving boosting tree

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Feature recognition of state signal of electromechanical integration railway turnout over health parameters using CMOS area array technology

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JOURNAL OF engineering-JOE 2022年第7期2022卷 715-724页

作者： Zhou, Yushuang Sharma, Ashutosh Shabaz, Mohammad Zhengzhou Railway Vocat & Tech Coll Sch Transportat & Management Zhengzhou Henan Peoples R China Southern Fed Univ Inst Comp Technol & Informat Secur Taganrog Russia Atha Minch Univ Arba Minch Ethiopia

Aim: Railway turnout is the most important line connection equipment of high-speed railway system. In order to improve the monitoring and maintenance of high-speed railway turnout gap, to avoid the large impact of train operation on the turnout. Background: With the increase of train speed, the impact of train operation on turnout is increasing. Therefore, the requirements for monitoring, maintenance, and adjustment of high-speed railway turnout gap are also gradually improved. Objective: In this paper, the feature recognition research of mechatronic railway turnout state signal based on CMOS area array technology is proposed. Methodology: First, it introduces the collection of turnout health parameters, the collection and processing of gap data in turnout based on CMOS, and the image feature recognition algorithm of adaptive threshold. The problem of inconvenient collecting the bifurcation image showing the gap is solved, and the pollution affects the accuracy. Results: The experimental results show that, according to the fault mechanism of turnout system, turnout faults are mainly divided into electrical faults and mechanical faults. On-site maintenance: the records show that the mechanical failure accounts for more than 90% of turnout failures. Fault category is the database of fault diagnosis model training. According to the structural principle and fault record description of turnout, there are 12 categories of turnout faults. Conclusion: The feature recognition of mechatronic railway turnout state signal based on CMOS area array technology proposed in this paper has certain validity and accuracy.

关键词： turnout faults fault diagnosis model training mechanical engineering computing high-speed railway system electromechanical integration railway turnout railways image feature recognition algorithm train speed railway engineering feature extraction mechatronics feature recognition research failure (mechanical) mechatronic railway turnout state signal train operation fault diagnosis high-speed railway turnout gap turnout health parameters turnout failures CMOS area array technology turnout system

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Explainable AI for Chiller Fault-Detection Systems: Gaining Human Trust

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COMPUTER 2021年第10期54卷 60-68页

作者： Srinivasan, Seshadhri Arjunan, Pandarasamy Jin, Baihong Sangiovanni-Vincentelli, Alberto Sultan, Zuraimi Poolla, Kameshwar Gen Elect Res Ctr Controls & Optimizat Bangalore 560068 Karnataka India Berkeley Educ Alliance Res Singapore 138602 Singapore Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Berkeley Educ Alliance Res Singapore Berkeley Bldg Efficiency & Sustainabil Singapore 138602 Singapore Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

We investigate the role of explainable Artificial Intelligence (XAI) for building trust in data-driven fault detection and diagnosis (FDD). We examine use cases for XAI-FDD on a building in Singapore that has six chil... 详细信息

关键词： Artificial Intelligence Condition Monitoring Cooling Fault Diagnosis HVAC mechanical engineering computing Building Trust XAI FDD Chillers Explainable AI Chiller Fault Detection Systems Human Trust Explainable Artificial Intelligence Singapore Data Driven Fault Detection And Diagnosis Fault Detection Buildings Artificial Intelligence

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A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

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IET SIGNAL PROCESSING 2021年第9期15卷 649-665页

作者： Abreu, Jose Oliveira, Roberto L. Fonseca Neto, Joao V. Fed Univ Para Technol Inst Comp Engn Dept Belem Para Brazil Fed Univ Para Comp Engn Belem Para Brazil Univ Fed Maranhao Elect Engn Dept Sao Luis Maranhao Brazil

One of the current ways to continue space research is to launch ballistic rockets that carry scientific payloads. To improve the accuracy of the instantaneous evolution of the payload impact on the Earths surface, it is necessary to estimate indirect measures more efficiently. This study proposes a comprehensive approach to determine the impact point prediction of ballistic rocket payloads. This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. Four existing stochastic estimators, namely a recursive Kalman filter (RKF), an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF) are compared with four proposed stochastic estimators. These include a recursive Kalman filter aided by an ANN (RKFN), an extended Kalman filter aided by an ANN (EKFN), an unscented Kalman filter aided by an ANN (UKFN), and a particle filter aided by an ANN (PFN). This study shows that the results that are obtained through the proposed tracking algorithms RKFN, EKFN, UKFN, and PFN are better than those of the existing tracking algorithms RKF, EKF, UKF, and PF. The proposed estimators can be efficient low-cost tools to mitigate inaccuracies during tracking up to the payload's impact.

关键词： payload impact recursive Kalman filter Aerospace industry nonlinear filters Digital signal processing artificial neural networks ballistic rocket payloads space research ANN stochastic processes particle filtering (numerical methods) particle filter unscented Kalman filter Statistics Fluid mechanics and aerodynamics (mechanical engineering) Filtering methods in signal processing ballistics Ballistics and mechanical impact (mechanical engineering) neural nets mechanical engineering computing rockets Kalman filters current ways extended Kalman filter mechanical engineering applications of IT artificial neural network models scientific payloads impact point prediction ballistic rockets Civil and mechanical engineering computing Other topics in statistics

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Extracting spatially global and local attentive features for rolling bearing fault diagnosis in electrical machines using attention stream networks

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IET ELECTRIC POWER APPLICATIONS 2021年第7期15卷 903-915页

作者： Karnavas, Yannis L. Plakias, Spyridon Chasiotis, Ioannis D. Democritus Univ Thrace Dept Elect & Comp Engn Elect Machines Lab Xanthi Greece

A health diagnosis mechanism of rolling element bearings is necessary since the most frequent faults in rotating electrical machines occur in the bearing parts. Recently, convolutional neural networks (CNNs) have redefined the state-of-the-art accuracy for bearing fault detection and identification, extracting location invariant feature mappings without the need for prior expert knowledge. With the use of convolution operations as the core of the process, CNNs consider the local spatial coherence of the input. However, one major drawback of the convolutional models is the weakness to capture global information about the input vector and to derive knowledge about the statistical properties of the latter. The authors propose a deep learning (DL) model that concatenates the features that are produced from two neural streams. Each consists of an attention mechanism that intends to learn different representations of the input vector, and so finally to produce a feature mapping that contains global and spatial locally information. Simulation results on two famous rolling element bearings fault detection benchmarks show the effectiveness of the method. In particular, the proposed DL model achieves 99.60% in the Case Western Reserve University bearing data set and 99.10% in the Paderborn University bearing data set.

关键词： local attentive features rolling element bearings fault detection benchmarks rolling bearings input vector locally information global attentive features convolutional neural networks attention stream networks deep learning model fault diagnosis neural streams deep learning (artificial intelligence) feature extraction health diagnosis mechanism bearing parts rotating electrical machines feature mapping CNNs convolutional neural nets attention mechanism convolution operations Paderborn University bearing data local spatial coherence bearing fault diagnosis location invariant feature mappings global information mechanical engineering computing condition monitoring Case Western Reserve University bearing data convolutional models

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A TEO-based modified Laplacian of Gaussian filter to detect faults in rolling element bearing for variable rotational speed machine

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2021年第2期15卷 193-203页

作者： Liu, Yi Jiang, Zhansi Huang, Haizhou Xiang, Jiawei Wenzhou Univ Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Guilin Univ Elect Technol Sch Mech & Elect Engn Guilin Peoples R China Huadian Elect Power Res Inst Hangzhou Peoples R China

Conventional signal processing techniques for fault detection are usually aimed at constant speed conditions. Nowadays, order tracking, especially tacho-less order tracking is regarded as a valuable tool for extracting fault features under variable rotational speed conditions. Therefore, a teager energy operator (TEO)-based modified Laplacian of Gaussian filter is proposed to enhance the fault detection behaviour of tacho-less order tracking. This method consists of four steps: First, multi-synchrosqueezing transform is employed to estimate and then extract the instantaneous rotation frequency of a shaft. Second, based on the extracted curve, the non-stationary domain signal is converted into a quasi-stationary domain by the re-sampling technique. Third, the obtained quasi-stationary domain signal is de-noise by the novel method. Finally, fault characteristic orders are extracted via envelope order spectrum analysis method and the performance is significantly improved. The results of numerical simulation and experimental investigations are performed to validate the superiority of the novel method for fault extraction under variable rotational speed conditions.

关键词： fault diagnosis Gaussian filter fault detection behaviour Reliability rolling bearings mechanical components signal sampling teager energy operator multisynchrosqueezing transform Signal processing and detection Inspection and quality control fault extraction Digital signal processing nonstationary domain signal element bearing Vibrations and shock waves (mechanical engineering) variable rotational speed conditions vibrational signal processing extracted curve fault features envelope order spectrum analysis method quasistationary domain signal instantaneous rotation frequency mechanical engineering computing mechanical engineering applications of IT Maintenance and reliability rolling element bearing constant speed conditions condition monitoring order tracking Civil and mechanical engineering computing fault characteristic orders TEO-based modified Laplacian of Gaussian filter to detect faults variable rotational speed machine vibrations

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Influence of dry run speed on the cleaning of compressor blade

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JOURNAL OF engineering-JOE 2021年第7期2021卷 359-365页

作者： Tang, Jie Gao, Dianrong Wang, Liwen Zeng, Jie Yanshan Univ Inst Mech Engn Qinhuangdao Hebei Peoples R China Civil Aviat Univ China Aviat Engn Inst Tianjin Peoples R China

To optimize the on-line cleaning process parameters of aero-engines, the influence of the engine's dry run Speed on the cleaning flow field was analysed. Through the reverse modelling and numerical simulation of the CFM56-7B compressor, the changes of static pressure, velocity and vorticity of the cleaning flow field under different dry run speeds were analysed. The results showed that the static pressure and speed of the cleaning flow field increased linearly with the increase of the dry run speed;the vortex of the cleaning flow field decreases with the increase of the speed, but the increase of the speed was beneficial to the cleaning of the compressor. Through the aero-engines online cleaning test, comparing the recovery of EGTM at different speeds, when the engine dry run speed was 323 r/min, a better cleaning effect and higher economic benefits can be obtained. The research can provide a theory for engine online cleaning.

关键词： vortices blades on-line cleaning process parameters cleaning test compressors aerospace computing static pressure Engines different dry run speeds Industrial processes engineering materials cleaning engine online cleaning cleaning effect Fluid mechanics and aerodynamics (mechanical engineering) surface cleaning cleaning flow field mechanical engineering computing mechanical engineering applications of IT engine dry run speed Maintenance and reliability aerospace engines jet engines compressor Civil and mechanical engineering computing Aerospace engineering computing aero-engines numerical analysis

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Analysis on fatigue life of overhead travelling crane girder under impact load for sustainable transport system

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第11期14卷 1426-1432页

作者： Yang, Mingliang Chang, Zhengyan Xu, Gening Wang, Chao Wang, Peng Taiyuan Univ Sci & Technol Sch Mech Engn Taiyuan 030024 Peoples R China

As the main bearing structure of the crane, the girder structure determines its service life. While the girder structure meets the strength, stiffness, and stability, its fatigue life should be paid more attention. A strong impact will be caused by the overhead travelling crane running through the uneven track joint. Based on SolidWorks and automated dynamic analysis of mechanical systems, the virtual prototype of 480 t overhead travelling crane operation is established in this study. The operation simulation of various track height differences is carried out. Compared with the theoretical calculation of GB and ISO, the virtual prototype is verified. Through the strain test and finite-element calculation of stress of girder under the two working conditions including the main car with a load of 433 t running to the mid-span and span end of the girder, the stress concentration in the transition rounded corner of the girder is found. On the basis of the simulation load spectrum and finite-element static stress analysis, combined with the S-N curve of material, the fatigue damage and life prediction model of the 480 t overhead travelling crane girder is established. The minimum life of the dangerous part is obtained, providing a basis for the design and maintenance.

关键词： mechanical engineering computing fatigue machine bearings beams (structures) cranes mechanical strength stress analysis finite element analysis maintenance engineering impact (mechanical) simulation load spectrum finite-element static stress analysis fatigue damage life prediction model crane girder fatigue life analysis impact load bearing structure girder structure automated dynamic analysis virtual prototype crane operation material S-N curve SolidWorks

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Method for automatic railway track surface defect classification and evaluation using a laser-based 3D model

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IET IMAGE PROCESSING 2020年第12期14卷 2701-2710页

作者： Ye, Jiaqi Stewart, Edward Zhang, Dingcheng Chen, Qianyu Roberts, Clive Univ Birmingham Dept Elect Elect & Syst Engn Birmingham B15 2TT W Midlands England

Inspection of physical surface defects is a significant concern in many industrial areas. In railway systems, this process mainly includes the detection and classification of defects in rails and wheels, for which laser-based optical inspection technologies have gradually been applied in the form of 2D profile measurement, benefiting from its high precision and robustness to surface conditions. However, defect classification and evaluation after the initial detection works still rely heavily on human inspectors to make maintenance suggestions. The linear nature of rails makes it possible to increase the dimension of rail measurement data from 2D to 3D by aligning 2D profiles along the rail, from which more comprehensive diagnosis information becomes available. In combination with appropriate artificial intelligence algorithms, this approach can potentially replace human-dominated defect classification and evaluation work. This study presents a 3D model-based railway track surface defect classification and evaluation method. A set of geometrical features are extracted from the 3D model of track surface defects to describe a distinguishable pattern for each category of defect. Multi-class classifiers are then tested and have shown promising results on a group of artificial track surface defects, giving a systemic solution for 3D model-based automatic track surface defect inspection.

关键词： flaw detection image classification artificial intelligence rails mechanical engineering computing railways maintenance engineering automatic optical inspection railway engineering feature extraction pattern classification wheels rail measurement data automatic railway track surface defect classification laser-based 3D model physical surface defects wheels laser-based optical inspection technologies 2D profile measurement rail maintenance artificial intelligence algorithms geometrical feature extraction multiclass classifiers

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