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| 1 | Intelligent Fault Diagnosis for Planetary Gearbox Using Transferable Deep Q Network Under Variable Conditions with Small Training Data显示文摘Effective fault diagnosis of planetary gearboxes is critical for ensuring the safety and dependability of mechanical drive systems.Nevertheless,variable conditions and inadequate fault data bring huge challenges to its practical fault diagnosis.Taking this into account,this study presents a new intelligent fault diagnosis(IFD)approach for planetary gearbox using a transferable deep Q network(TDQN)that merges deep reinforcement learning(DRL)and transfer learning(TL).First,a DRL environment simulation is designed by a predefined classification Markov decision process.Then,leveraging varied-size convolutions and residual learning,a multiscale residual convolutional neural network agent for TDQN is created to automatically learn meaningful features directly from vibration signals while avoiding model degradation.Next,a large source dataset is obtained from complex conditions,and this agent learns an IFD policy via autonomous interaction with the data environment.Finally,a parameter-based TL strategy is adopted to retrain the model on target datasets with variable conditions and small training data,which is conducted by fine-tuning the model parameters gained from the source task to accomplish target tasks.The results show that this TDQN outperforms not only state-of-the-art methods in a source task with an accuracy of 98.53%but also in two target tasks with 99.63%and 98.37%,respectively. | Hui Wang Jiawen Xu Ruqiang Yan | 2023 | Journal of Dynamics, Monitoring and Diagnostics2023，2，1： | 1 |
| 2 | Deep Residual Joint Transfer Strategy for Cross-Condition Fault Diagnosis of Rolling Bearings显示文摘Rolling bearings are key components of the drivetrain in wind turbines,and their health is critical to wind turbine operation.In practical diagnosis tasks,the vibration signal is usually interspersed with many disturbing components,and the variation of operating conditions leads to unbalanced data distribution among different conditions.Although intelligent diagnosis methods based on deep learning have been intensively studied,it is still challenging to diagnose rolling bearing faults with small amounts of samples.To address the above issue,we introduce the deep residual joint transfer strategy method for the cross-condition fault diagnosis of rolling bearings.One-dimensional vibration signals are pre-processed by overlapping feature extraction techniques to fully extract fault characteristics.The deep residual network is trained in training tasks with sufficient samples,for fault pattern classification.Subsequently,three transfer strategies are used to explore the generalizability and adaptability of the pre-trained models to the data distribution in target tasks.Among them,the feature transferability between different tasks is explored by model transfer,and it is validated that minimizing data differences of tasks through a dual-stream adaptation structure helps to enhance generalization of the models to the target tasks.In the experiments of rolling bearing faults with unbalanced data conditions,localized faults of motor bearings and planet bearings are successfully identified,and good fault classification results are achieved,which provide guidance for the cross-condition fault diagnosis of rolling bearings with small amounts of training data. | Songjun Han Zhipeng Feng | 2023 | Journal of Dynamics, Monitoring and Diagnostics2023，2，1： | 1 |
| 3 | Exploration of structural damaging of steel specimens by the metal magnetic memory method显示文摘 | Doubov A A Demin E A | 2000 | Monitoring & Diagnostics2000，，7： | 1 |
| 4 | Condition monitoring of bearing using ESP 显示文摘 | Mcmahon S | 1991 | Condi-tion Monitoring and Diagnostic Technology1991，2，1： | 1 |
| 5 | Helicopter health monitoring and failure prevention through vibration management enhancement program 显示文摘 | Victor G Paul G Dariusz W | 2001 | Condition Monitoring & Diagnostic Engineering Management2001，4，4： | 1 |
| 6 | An Improved Dynamic Modelling for Exploring Ball Bearing Vibrations from Time-Varying Oil Film显示文摘Bearings are key components in rotating machinery,which is widely used in many fields,such as CNC machines,wind turbines and induction machines.The increasingly harsh operation environment can lead to wear and tear on raceways and reduce the precision and reliability of bearing or even machinery.Lubrication could relieve the wear to some degree,which is benefit to prolong the bearing’s life.Thus,investigation on the vibration responses under the influence of oil film is of great significance.However,for mechanism analysis,how to include the oil film into the bearing dynamic model affects the result and efficiency of solution.To address this problem,this study proposed a fast algorithm through load distribution and interpolation when calculating oil film stiffness and thickness during the solution of bearing vibration model.Analysis of oil film on vibration is carried out and a bearing test rig is designed to verify the proposed model.Numerical simulation result shows that rotational speed and load have vital effect on oil film and vibration.The experimental result is consistent with the simulation,which shows that the proposed model has a better performance on modeling bearing vibration and the method of considering oil film is reasonable. | Minmin Xu Zhenzhen Song Xiaoxi Ding Guoxing Li Yimin Shao James Xi Gu | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，2： | 1 |
| 7 | Active sensors for health monitoring of aging aerospace structures显示文摘 | Giurgiutiu V Zagrai A Bao J | 2003 | International Journal of the Condition Monitoring and Diagnostic Engineering Management2003，6，1： | 1 |
| 8 | Computational study on plate damage identification显示文摘 | Stephanie A | 2002 | NDE for Health Monitoring and Diagnostics San Diego2002，47，： | 1 |
| 9 | Toward an integration of item-response theory and cognitive error diagnosis显示文摘 | Tatsuoka K K | 1990 | Diagnostic monitoring of skill and knowledge acquisition1990，，： | 1 |
| 10 | Forecasting Machine Condition Using Grey System Theory 显示文摘 | Luo M Kuhnell B T | 1991 | Condition Monitoring and Diagnostic Technology1991，1，3： | 1 |
| 11 | Identification of hydraulic system malfunctions using ferrography 显示文摘 | Prakash A Gandhi O P | 1991 | Condition Monitoring & Diagnostic Technology1991，，2： | 1 |
| 12 | Investigation of pH and temperature on optical rotatory dispersion for noninvasive glucose monito- ring 显示文摘 | BABA J S MELEDEO A CAMERON B D | 2001 | Optical Diagnostics and Sensing of Biologi- cal Fluids and Glucose and Cholesterol Monitoring2001，20，2： | 1 |
| 13 | Active Sensors for Health Monitoring ofAging Aerospace Structures显示文摘 | Giurgiutiu V | 2003 | International Journalof Condition Monitoring&Diagnostic EngineeringManagement2003，6，1： | 1 |
| 14 | Plant residual time distribution prediction using expert judgments based condition monitoring Information显示文摘 | Wang W Zhang W | 2001 | Condition Monitoring and Diagnostic Engineering Management2001，，： | 1 |
| 15 | Automated Safety Monitoring: A Review and Classification of Methods显示文摘 | Papadopoulos Y McDemid J | 2001 | International Jouranl of Condition Monitoring and Diagnostic Engineering Management2001，4，： | 1 |
| 16 | Forecasting machine condition using grey system theory显示文摘 | Luo M Kuhnell B T | 1991 | Condition Monitoring and Diagnostic Technology1991，1，3： | 1 |
| 17 | Prognostics and Remaining Useful Life Prediction of Machinery:Advances,Opportunities,and Challenges显示文摘As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last decades.In this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,etc.Based on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development. | JDMD Editorial Office Nagi Gebraeel Yaguo Lei Naipeng Li Xiaosheng Si Enrico Zio | 2023 | Journal of Dynamics, Monitoring and Diagnostics2023，2，1： | 1 |
| 18 | Preface显示文摘Diagnostics and condition monitoring are aspects of a field of engineering that aims to manage the health of engineering machinery and structures,but which has increasing applicability to many other complex systems in the world from the medical care of people,through the reliability of supply chain logistics to the secure operation of a large and complex organisation.The management of health generally requires a number of sequential steps,which are usually as follows:the detection of any abnormality in the procedural operation of the system,the location of the abnormal behaviour within the system,an assessment of the severity of the abnormality and identification of the potential system vulnerabilities that result from it,a detailed diagnosis of the problem including the identification of its root cause,and finally a predictive assessment of the future prospects for the continued operation of the system,including any remedial action that should be taken to minimise impact and maximise continued operational performance. | Fulei Chu Andrew Ball | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，1： | 0 |
| 19 | Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification显示文摘Recently,deep learning(DL)has been widely used in the field of remaining useful life(RUL)prediction.Among various DL technologies,recurrent neural network(RNN)and its variant,e.g.,long short-term memory(LSTM)network,have gained extensive attention for their ability to capture temporal dependence.Although existing RNN-based methods have demonstrated their RUL prediction effectiveness,they still suffer from the following two limitations:1)it is difficult for the RNN to directly extract degradation features from original monitoring data and 2)most RNN-based prognostics methods are unable to quantify RUL uncertainty.To address the aforementioned limitations,this paper proposes a new prognostics method named residual convolution LSTM(RC-LSTM)network.In the RC-LSTM,a new ResNet-based convolution LSTM(Res-ConvLSTM)layer is stacked with a convolution LSTM(ConvLSTM)layer to extract degradation representations from monitoring data.Then,under the assumption that the RUL follows a normal distribution,an appropriate output layer is constructed to quantify the uncertainty of prediction results.Finally,the effectiveness and superiority of the RC-LSTM are verified using monitoring data from accelerated bearing degradation tests. | Wenting Wang Yaguo Lei Tao Yan Naipeng Li Asoke KNandi | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，1： | 0 |
| 20 | The Effect of Signal Propagation Delay on the Measured Vibration in Planetary Gearboxes显示文摘Modulation of the gear mesh vibration is a major field of research for the condition monitoring of planetary gearboxes.The modulation creates sidebands around the gearmesh frequency in the vibration spectrum,and the distribution of these sidebands has been researched in numerous papers.All publications on the subject assume that the effect of the time varying signal propagation delay between the main vibration source–the gear mesh point(s)–and the(usually fixed)transducer can be neglected.This paper investigates the validity of this assumption.To do so,a planetary gearbox with a transducer mounted on the(fixed)ring gear is studied,and the effect of the propagation delay is modelled as a phase modulation of the gear mesh vibration.General expressions are then derived for the distribution and strength of the modulation sidebands,and these expressions are applied to quantify the effect of the propagation delay on five industrial gearboxes.The results show that the amplitude of the sidebands is negligible and would not interfere with condition assessment based on analysis of the modulation of the gear mesh frequency,and thus the propagation delay can be neglected for practical purposes. | Marc Hilbert Wade A.Smith Robert B.Randall | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，1： | 0 |
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