Pub Date : 2023-02-17DOI: 10.1177/1748006x231153704
Yangfan Li, Yingjie Zhang, Ning An
To evaluate the accuracy reliability of machine tools, an evaluation method of accuracy reliability considering the machining error out of tolerance is proposed, and an accurate mathematical description is given to realize the quantitative evaluation of machining accuracy under different machining loads. On this basis, the basic connotation and mathematical model of accuracy retention are analyzed, and the influence of different machining loads on the accuracy retention is studied. Finally, experiments are carried out to detect the variations of machining errors of machine tools under different loads and the quantitative evaluations of accuracy reliability and retention of machine tools are completed, which verify the validity of the proposed methods.
{"title":"Accuracy reliability analysis of CNC machine tools considering manufacturing errors degrees","authors":"Yangfan Li, Yingjie Zhang, Ning An","doi":"10.1177/1748006x231153704","DOIUrl":"https://doi.org/10.1177/1748006x231153704","url":null,"abstract":"To evaluate the accuracy reliability of machine tools, an evaluation method of accuracy reliability considering the machining error out of tolerance is proposed, and an accurate mathematical description is given to realize the quantitative evaluation of machining accuracy under different machining loads. On this basis, the basic connotation and mathematical model of accuracy retention are analyzed, and the influence of different machining loads on the accuracy retention is studied. Finally, experiments are carried out to detect the variations of machining errors of machine tools under different loads and the quantitative evaluations of accuracy reliability and retention of machine tools are completed, which verify the validity of the proposed methods.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84059574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-06DOI: 10.1177/1748006x231152633
H. Mehanpour, S. R. Hoseini Vaez, M. A. Fathali
There are two general methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) for the optimal design of structures with non-deterministic variables. In the problem-solving process of both RDO and RBRDO, assessing the probabilistic constraints of the problem and the standard deviation of the structural responses is time-consuming and costly. In this study, by presenting a multi-step approach, for a probable optimal solution, the deterministic constraints are first investigated. Then the limit state functions related to probabilistic constraints are computed based on the mean value of random variables (in the RBRDO problem) and in the step of uncertainties effects simulation on the response of the structure, probabilistic constraints are evaluated (in the RBRDO problem). Designs that not meeting the deterministic constraints or in the unsafe area for the mean values of the random variables are not included in calculating the first and second terms of the objective function and reliability assessment. This leads to reduce the volume of calculations. The Monte Carlo simulation method is used to assess the probabilistic constraints, and the EVPS metaheuristic algorithm is used for the optimization process. Three benchmark trusses of 10, 25, and 72 bars were studied to assess the efficiency of the proposed approach. The first objective function in these problems was the mean weight of trusses and the second objective function was the standard deviation of the displacement of a specific node in a certain direction.
{"title":"A multi-step approach for reliability-based robust design optimization of truss structures","authors":"H. Mehanpour, S. R. Hoseini Vaez, M. A. Fathali","doi":"10.1177/1748006x231152633","DOIUrl":"https://doi.org/10.1177/1748006x231152633","url":null,"abstract":"There are two general methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) for the optimal design of structures with non-deterministic variables. In the problem-solving process of both RDO and RBRDO, assessing the probabilistic constraints of the problem and the standard deviation of the structural responses is time-consuming and costly. In this study, by presenting a multi-step approach, for a probable optimal solution, the deterministic constraints are first investigated. Then the limit state functions related to probabilistic constraints are computed based on the mean value of random variables (in the RBRDO problem) and in the step of uncertainties effects simulation on the response of the structure, probabilistic constraints are evaluated (in the RBRDO problem). Designs that not meeting the deterministic constraints or in the unsafe area for the mean values of the random variables are not included in calculating the first and second terms of the objective function and reliability assessment. This leads to reduce the volume of calculations. The Monte Carlo simulation method is used to assess the probabilistic constraints, and the EVPS metaheuristic algorithm is used for the optimization process. Three benchmark trusses of 10, 25, and 72 bars were studied to assess the efficiency of the proposed approach. The first objective function in these problems was the mean weight of trusses and the second objective function was the standard deviation of the displacement of a specific node in a certain direction.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90943668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-28DOI: 10.1177/1748006x221151094
Miaomiao Yu, Yinghui Tang
We consider a shock and wear model in which the inter-shock arrival process is a phase-type (PH) renewal process, and the system’s lifetime is generally distributed. The system has two competing failure modes. One failure mode is due to random shocks, which cause failure by overloading the system. The other failure mode is owning to wear-out failures, which usually happen after the system has run for many cycles. System failure is not self-announcing and remains undiscovered unless an inspection is performed. The intervals between successive inspections are identical and equal to T time units. If a system failure is detected, the corrective repair or replacement is conducted immediately. If the system is found working at inspection, preventive maintenance will be carried out to prolong its useful life. Furthermore, to model the occurrence of events with an underlying monotonic trend, the extended geometric process (EGP) is employed to account for the impact of different types of failures on the system’s degradation. Moreover, for establishing the cost rate function in our model, the counting process generated from a PH renewal shock process is studied in detail using the roots method and formula for calculating residues. Based on these results, the survival function and other characteristics of the system are further investigated. Finally, numerical examples that determine the optimal inspection period T[Formula: see text] and the optimal replacement policy N[Formula: see text], which minimizes the long-run average cost rate, are presented.
{"title":"Analyze periodic inspection and replacement policy of a shock and wear model with phase-type inter-shock arrival times using roots method","authors":"Miaomiao Yu, Yinghui Tang","doi":"10.1177/1748006x221151094","DOIUrl":"https://doi.org/10.1177/1748006x221151094","url":null,"abstract":"We consider a shock and wear model in which the inter-shock arrival process is a phase-type (PH) renewal process, and the system’s lifetime is generally distributed. The system has two competing failure modes. One failure mode is due to random shocks, which cause failure by overloading the system. The other failure mode is owning to wear-out failures, which usually happen after the system has run for many cycles. System failure is not self-announcing and remains undiscovered unless an inspection is performed. The intervals between successive inspections are identical and equal to T time units. If a system failure is detected, the corrective repair or replacement is conducted immediately. If the system is found working at inspection, preventive maintenance will be carried out to prolong its useful life. Furthermore, to model the occurrence of events with an underlying monotonic trend, the extended geometric process (EGP) is employed to account for the impact of different types of failures on the system’s degradation. Moreover, for establishing the cost rate function in our model, the counting process generated from a PH renewal shock process is studied in detail using the roots method and formula for calculating residues. Based on these results, the survival function and other characteristics of the system are further investigated. Finally, numerical examples that determine the optimal inspection period T[Formula: see text] and the optimal replacement policy N[Formula: see text], which minimizes the long-run average cost rate, are presented.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84518991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-28DOI: 10.1177/1748006X221125865
T. Aven
{"title":"Is the definition of risk still contested?","authors":"T. Aven","doi":"10.1177/1748006X221125865","DOIUrl":"https://doi.org/10.1177/1748006X221125865","url":null,"abstract":"","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79982499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1177/1748006x221146367
B. Abba, Hong Wang
This paper presents an additive Gompertz-Weibull (AGW) distribution, a four-parameter hybrid probability distribution, and its applications in reliability engineering. The failure rate (FR) function of the proposed model demonstrates an increasing trend and a variety of bathtub shapes with or without a low and yet long-stable segment, making it appropriate for modelling a wide variety of real-world problems. Some relationships between the AGW’s FR and its mean residual life functions are examined. For parameter estimation, maximum likelihood and Bayesian inferences are considered. For posterior simulations, we use Hamiltonian Monte Carlo to evaluate the Bayes estimators of the AGW parameters. We evaluate the performance of the proposed AGW model to that of other recent bathtub distributions constructed following the same approach on three failure time datasets. The first two datasets represent device failure times, while the third represents early cable-joint failure times, all with bathtub FR. For comparison, five parametric and nonparametric evaluation criteria and the fitted FR and mean residual life curves were employed. The results indicated that the AGW model would be the best choice for describing failure times, especially when the bathtub-shaped FR of the presented dataset exhibits its three segments.
{"title":"A new failure times model for one and two failure modes system: A Bayesian study with Hamiltonian Monte Carlo simulation","authors":"B. Abba, Hong Wang","doi":"10.1177/1748006x221146367","DOIUrl":"https://doi.org/10.1177/1748006x221146367","url":null,"abstract":"This paper presents an additive Gompertz-Weibull (AGW) distribution, a four-parameter hybrid probability distribution, and its applications in reliability engineering. The failure rate (FR) function of the proposed model demonstrates an increasing trend and a variety of bathtub shapes with or without a low and yet long-stable segment, making it appropriate for modelling a wide variety of real-world problems. Some relationships between the AGW’s FR and its mean residual life functions are examined. For parameter estimation, maximum likelihood and Bayesian inferences are considered. For posterior simulations, we use Hamiltonian Monte Carlo to evaluate the Bayes estimators of the AGW parameters. We evaluate the performance of the proposed AGW model to that of other recent bathtub distributions constructed following the same approach on three failure time datasets. The first two datasets represent device failure times, while the third represents early cable-joint failure times, all with bathtub FR. For comparison, five parametric and nonparametric evaluation criteria and the fitted FR and mean residual life curves were employed. The results indicated that the AGW model would be the best choice for describing failure times, especially when the bathtub-shaped FR of the presented dataset exhibits its three segments.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85083055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a novel contracting approach, the performance-based contracting (PBC) utilizes defined performance goals and structured incentives to improve the system availability and reduce the cost by tying the compensation to the service supplier to the system performance outcome. In this paper, two new CBM optimization models within the PBC framework are proposed considering the impacts of destructive inspections on the system degradation behavior. The average maintenance cost rate and the system availability within the contract horizon are estimated. Under the inspection-based replacement scheme, the objectives are to maximize the expected profit rate to the supplier and/or the resulting system average availability. A solution procedure based on the non-dominated sorting genetic algorithm (NSGA-II) combining with the weighted sum method (WSM) is introduced to derive the optimal CBM policies. Numerical examples and sensitivity analysis are conducted to examine the applicability of the proposed models.
{"title":"Condition-based maintenance optimization for deteriorating systems considering performance-based contracting and destructive inspections","authors":"Yu-Jun Zhang, Yukun Wang, Xiaopeng Li, Yiliu Liu, Weizheng Gao","doi":"10.1177/1748006x221148239","DOIUrl":"https://doi.org/10.1177/1748006x221148239","url":null,"abstract":"As a novel contracting approach, the performance-based contracting (PBC) utilizes defined performance goals and structured incentives to improve the system availability and reduce the cost by tying the compensation to the service supplier to the system performance outcome. In this paper, two new CBM optimization models within the PBC framework are proposed considering the impacts of destructive inspections on the system degradation behavior. The average maintenance cost rate and the system availability within the contract horizon are estimated. Under the inspection-based replacement scheme, the objectives are to maximize the expected profit rate to the supplier and/or the resulting system average availability. A solution procedure based on the non-dominated sorting genetic algorithm (NSGA-II) combining with the weighted sum method (WSM) is introduced to derive the optimal CBM policies. Numerical examples and sensitivity analysis are conducted to examine the applicability of the proposed models.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89398879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1177/1748006x221147331
S. Ashrafi, M. Asadi, Razieh Rostami
In this paper, we investigate optimal age-based preventive maintenance (PM) policies for an ( n- k + 1)-out-of- n system whose components are exposed to fatal shocks that arrive from various sources. We consider two different scenarios for the system failure. In the first one, it is assumed that the shock process is of the type of Marshall-Olkin where each shock affects one component of the system and puts it down, and one shock affects all components and destroys all of them. In the second scenario, it is assumed that the system is subject to an extended type of Marshall-Olkin shock process where the shocks arriving at random times may cause the breakdown of 1, 2, …, or n components. Under each scenario for the components failure, we investigate an optimal age-based PM model for the system by imposing the related cost function. Then, in each case, we explore the optimal PM time that minimizes the mean cost per unit of time. Some numerical results are presented to illustrate the applications of the proposed models.
在本文中，我们研究了一个(n- k + 1)- of- n系统的最佳基于年龄的预防性维护(PM)策略，该系统的组件暴露于来自各种来源的致命冲击。我们考虑两种不同的系统故障场景。在第一个模型中，假设冲击过程属于马歇尔-奥尔金模式，即每次冲击影响系统的一个组件并使其失效，一次冲击影响所有组件并摧毁它们。在第二种情况下，假设系统受到扩展类型的Marshall-Olkin冲击过程的影响，其中随机时间到达的冲击可能导致1、2、…或n个组件的击穿。在组件失效的每种情况下，我们通过施加相关的成本函数来研究系统的最优基于年龄的PM模型。然后，在每种情况下，我们探索使每单位时间的平均成本最小化的最佳PM时间。给出了一些数值结果来说明所提模型的应用。
{"title":"On preventive maintenance of k-out-of-n systems subject to fatal shocks","authors":"S. Ashrafi, M. Asadi, Razieh Rostami","doi":"10.1177/1748006x221147331","DOIUrl":"https://doi.org/10.1177/1748006x221147331","url":null,"abstract":"In this paper, we investigate optimal age-based preventive maintenance (PM) policies for an ( n- k + 1)-out-of- n system whose components are exposed to fatal shocks that arrive from various sources. We consider two different scenarios for the system failure. In the first one, it is assumed that the shock process is of the type of Marshall-Olkin where each shock affects one component of the system and puts it down, and one shock affects all components and destroys all of them. In the second scenario, it is assumed that the system is subject to an extended type of Marshall-Olkin shock process where the shocks arriving at random times may cause the breakdown of 1, 2, …, or n components. Under each scenario for the components failure, we investigate an optimal age-based PM model for the system by imposing the related cost function. Then, in each case, we explore the optimal PM time that minimizes the mean cost per unit of time. Some numerical results are presented to illustrate the applications of the proposed models.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72806444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1177/1748006x221141744
Jin Cui, Licai Cao, Tianxiao Zhang
Bearing is one of the most important supporting components in mechanical equipment and its health status has a significant impact on the overall performance of equipment. The remaining useful life (RUL) prediction of bearings is critical in adopting a condition-based maintenance strategy to ensure reliable equipment operation. To accurately predict the RUL of bearings, this paper proposes a two-stage Gaussian process regression (GPR) model, which combines the flexibility of the Gaussian process and the physical mechanism of the Wiener process. Compared with the conventional GPR model, the proposed model can reasonably adapt to the statistical characteristics of bearings degradation and provide more stable predictions. In addition, the paper proposes a new degradation detection approach based on the Euclidean distance to distinguish the two stages of the bearing service life cycle, which considers the global characteristics of bearing degradation and can accurately detect the beginning point of bearing degradation. The experimental results show that the proposed two-stage GPR model can help to improve the precision and accuracy of degradation path tracking and RUL prediction.
{"title":"A two-stage Gaussian process regression model for remaining useful prediction of bearings","authors":"Jin Cui, Licai Cao, Tianxiao Zhang","doi":"10.1177/1748006x221141744","DOIUrl":"https://doi.org/10.1177/1748006x221141744","url":null,"abstract":"Bearing is one of the most important supporting components in mechanical equipment and its health status has a significant impact on the overall performance of equipment. The remaining useful life (RUL) prediction of bearings is critical in adopting a condition-based maintenance strategy to ensure reliable equipment operation. To accurately predict the RUL of bearings, this paper proposes a two-stage Gaussian process regression (GPR) model, which combines the flexibility of the Gaussian process and the physical mechanism of the Wiener process. Compared with the conventional GPR model, the proposed model can reasonably adapt to the statistical characteristics of bearings degradation and provide more stable predictions. In addition, the paper proposes a new degradation detection approach based on the Euclidean distance to distinguish the two stages of the bearing service life cycle, which considers the global characteristics of bearing degradation and can accurately detect the beginning point of bearing degradation. The experimental results show that the proposed two-stage GPR model can help to improve the precision and accuracy of degradation path tracking and RUL prediction.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81462952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1177/1748006x221147784
Jia Luo, Jingying Huang, Jiancheng Ma, Siyuan Liu
The Generative Adversarial Network (GAN) can generate samples similar to the original data to solve the problem of fault sample imbalance in planetary gearbox fault diagnosis. Most of models rely heavily on convolution to model the dependencies across feature vectors of vibration signals. However, the characterization ability of convolution operator is limited by the size of convolution kernel and it cannot capture the long-distance dependence in the original data. In this paper, self-attention is introduced into Conditional Deep Convolutional Generative Adversarial Networks (C-DCGAN). In the model, vibration features are dynamically weighted and merged, so that it can adaptively focus “attention” on different times to solve the problem of sample differences caused by time-varying vibration signals. Finally, the proposed method is verified on the planetary gearbox experiment and the quality of the generated signal samples is evaluated with Dynamic Time Warping (DTW) algorithm. The visual experimental results indicated that the proposed model performed better than conditional deep convolutional generative adversarial networks (C-DCGAN) and could accurately diagnose various working states of planetary gearboxes.
生成对抗网络(GAN)可以生成与原始数据相似的样本，解决行星齿轮箱故障诊断中故障样本不平衡的问题。大多数模型严重依赖于卷积来模拟振动信号特征向量之间的依赖关系。然而，卷积算子的表征能力受到卷积核大小的限制，无法捕捉原始数据中的远距离依赖关系。将自注意引入到条件深度卷积生成对抗网络(C-DCGAN)中。在模型中，对振动特征进行动态加权和合并，使其能够自适应地将“注意力”集中到不同的时间，解决了振动信号时变导致的样本差异问题。最后，通过行星齿轮箱实验验证了该方法的有效性，并利用动态时间翘曲(Dynamic Time warp, DTW)算法对生成的信号样本质量进行了评价。视觉实验结果表明，该模型优于条件深度卷积生成对抗网络(C-DCGAN)，能够准确诊断行星齿轮箱的各种工作状态。
{"title":"Application of self-attention conditional deep convolutional generative adversarial networks in the fault diagnosis of planetary gearboxes","authors":"Jia Luo, Jingying Huang, Jiancheng Ma, Siyuan Liu","doi":"10.1177/1748006x221147784","DOIUrl":"https://doi.org/10.1177/1748006x221147784","url":null,"abstract":"The Generative Adversarial Network (GAN) can generate samples similar to the original data to solve the problem of fault sample imbalance in planetary gearbox fault diagnosis. Most of models rely heavily on convolution to model the dependencies across feature vectors of vibration signals. However, the characterization ability of convolution operator is limited by the size of convolution kernel and it cannot capture the long-distance dependence in the original data. In this paper, self-attention is introduced into Conditional Deep Convolutional Generative Adversarial Networks (C-DCGAN). In the model, vibration features are dynamically weighted and merged, so that it can adaptively focus “attention” on different times to solve the problem of sample differences caused by time-varying vibration signals. Finally, the proposed method is verified on the planetary gearbox experiment and the quality of the generated signal samples is evaluated with Dynamic Time Warping (DTW) algorithm. The visual experimental results indicated that the proposed model performed better than conditional deep convolutional generative adversarial networks (C-DCGAN) and could accurately diagnose various working states of planetary gearboxes.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90791276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1177/1748006x221147441
Hai Li, Chaoqun Wang
Limited data are common in the problem of remaining life prediction (RUL) of rolling bearings, and the distribution of degradation data of rolling bearings under different working conditions is quite different, which makes it difficult to predict the RUL of rolling bearings with limited data. To address this issue, this study combines first prediction time identification (FPT) and time-series feature window (TSFW) for predicting the RUL of rolling bearings with limited data. Firstly, the proper first prediction time is identified by a novel FPT identification method considering root mean square and Kurtosis simultaneously. Subsequently, to accurately capture the sequential characteristics of bearing degradation data, the TSFW is constructed and then adaptively compressed considering degradation factor that is derived mathematically. Based on this, this study employs multi-step ahead rolling prediction strategy with degradation factor from FPT to reveal the future degradation trend and then predict the bearing RUL. Finally, the feasibility and generalization of the proposed method under limited data is validated by carrying out several rolling bearing experiments, and the prediction errors for two representative bearings are 14.46% and 8.06%.
{"title":"Combining first prediction time identification and time-series feature window for remaining useful life prediction of rolling bearings with limited data","authors":"Hai Li, Chaoqun Wang","doi":"10.1177/1748006x221147441","DOIUrl":"https://doi.org/10.1177/1748006x221147441","url":null,"abstract":"Limited data are common in the problem of remaining life prediction (RUL) of rolling bearings, and the distribution of degradation data of rolling bearings under different working conditions is quite different, which makes it difficult to predict the RUL of rolling bearings with limited data. To address this issue, this study combines first prediction time identification (FPT) and time-series feature window (TSFW) for predicting the RUL of rolling bearings with limited data. Firstly, the proper first prediction time is identified by a novel FPT identification method considering root mean square and Kurtosis simultaneously. Subsequently, to accurately capture the sequential characteristics of bearing degradation data, the TSFW is constructed and then adaptively compressed considering degradation factor that is derived mathematically. Based on this, this study employs multi-step ahead rolling prediction strategy with degradation factor from FPT to reveal the future degradation trend and then predict the bearing RUL. Finally, the feasibility and generalization of the proposed method under limited data is validated by carrying out several rolling bearing experiments, and the prediction errors for two representative bearings are 14.46% and 8.06%.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72859603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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