With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.
{"title":"Key components identification of EMU complex system faults with interval intuitionistic fuzzy set and multi-attribute group decision-making based on FMECA method","authors":"Yuchen Zhang, Jinghui Liu, Chengye Dai, Qiufen Li, Zhan Guo, Xianchun Dai","doi":"10.1177/1748006x241262831","DOIUrl":"https://doi.org/10.1177/1748006x241262831","url":null,"abstract":"With the continuous acceleration of high-speed railway, the high-voltage traction system of the EMU is an important part for ensuring the operation speed and safety. If the failure does not discontinued effectively, it will cause major dangerous accidents, so the key components identification of system is crucial. This paper focus on the contradictions of the expert evaluation information ambiguity, the difference of expert risk appetite and the rationality of risk priority number (RPN) calculation method in the traditional failure analysis method FMECA. The interval intuitionistic fuzzy set (IIFS) is introduced to transform the expert evaluation into the form of membership interval and non-membership interval, which reduced the ambiguity of the specific numerical score. The interval intuitive fuzzy entropy was used to determine the entropy values of the occurrence (O), severity (S), and undetectable degree (D) of each failure mode under each expert score, which was used to calculate the weight value [Formula: see text], to weaken the influence caused by subjective risk preference. The interval intuition fuzzy ensemble operator (AIVIFWM) is used to assemble a single scoring matrix into a comprehensive score, which weakens the subjective influence of expert evaluation. Combined with the multi-attribute group decision-making idea, the score function [Formula: see text] is calculated for each comprehensive evaluation interval of each failure mode after assembly, so as to sort the failure mode risk and finally identify the key components. Based on the fault data of the high-voltage traction system of a certain type of EMU in 2022, 39 failure modes of 30 components are researched and summarized. The results show that rectifier, converter cooling unit, and carbon skateboard are the key components of EMU high-voltage traction system, which provided basic support for the detection and maintenance decision.","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":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784020","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 : 2024-07-23DOI: 10.1177/1748006x241261126
Meiqi Huang, Faqun Qi, Lin Wang
This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.
{"title":"Novel condition-based opportunistic maintenance policy for series systems with dependent competing failure processes","authors":"Meiqi Huang, Faqun Qi, Lin Wang","doi":"10.1177/1748006x241261126","DOIUrl":"https://doi.org/10.1177/1748006x241261126","url":null,"abstract":"This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.","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":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784014","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 : 2024-07-23DOI: 10.1177/1748006x241260997
Cheng Zhou, Da Xu, Zhaoyang Wang
To ensure the accuracy of military equipment maintainability verification results, a sample allocation method for maintainability test based on extended failure mode, effects and criticality analysis (FMECA) information is proposed to address the problem that the influence factors are relatively one-sided and the weighting is subjective in the current test sample allocation method. The influence factors are analyzed and determined from three aspects: the fault occurrence characteristics, fault impact, and test economy. A method for calculating the fault propagation intensity based on the improved LeaderRank is proposed, which is introduced into the FMECA for expansion. It is proposed to use the CRITIC method to determine the objective weights of the influence factors, determine the subjective weights based on the relative importance of the influence factors, introduce game equilibrium to weigh and determine the combination weights, and then complete the sample allocation. Finally, an example showed that the influence factors determined by this method are more comprehensive, the weight results are more scientific, and the sample structure is more reasonable.
{"title":"Sample allocation method for maintainability test based on extended FMECA information","authors":"Cheng Zhou, Da Xu, Zhaoyang Wang","doi":"10.1177/1748006x241260997","DOIUrl":"https://doi.org/10.1177/1748006x241260997","url":null,"abstract":"To ensure the accuracy of military equipment maintainability verification results, a sample allocation method for maintainability test based on extended failure mode, effects and criticality analysis (FMECA) information is proposed to address the problem that the influence factors are relatively one-sided and the weighting is subjective in the current test sample allocation method. The influence factors are analyzed and determined from three aspects: the fault occurrence characteristics, fault impact, and test economy. A method for calculating the fault propagation intensity based on the improved LeaderRank is proposed, which is introduced into the FMECA for expansion. It is proposed to use the CRITIC method to determine the objective weights of the influence factors, determine the subjective weights based on the relative importance of the influence factors, introduce game equilibrium to weigh and determine the combination weights, and then complete the sample allocation. Finally, an example showed that the influence factors determined by this method are more comprehensive, the weight results are more scientific, and the sample structure is more reasonable.","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":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784016","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 : 2024-06-22DOI: 10.1177/1748006x241252469
Dongdong Wei, Ming Jian Zuo, Zhigang Tian
Traditional Neural Networks (NNs) trained in a one-stage process often struggle to perform well when presented with new classes or domain shifts in testing datasets. In fault diagnosis, it is essential to handle a sequence of diagnostic tasks with new fault classes and working conditions. This paper presents a multi-staged Continual Learning algorithm that learns from a sequence of diagnostic tasks. In each training stage, a small portion of previously seen training data is incorporated to help the model remember old tasks and better learn new tasks. A novel scheme is designed to select previously seen data from multiple old tasks, considering their different working conditions. A multi-way domain adaptation is then conducted to mitigate the impact of multiple changes in working conditions among different tasks. The proposed method is tested using two different experiment test rigs, including both gear and bearing faults. Results demonstrate that the proposed Continual Learning algorithm allows NNs to learn from a sequence of diagnostics tasks efficiently and maintain high accuracies for all the tasks of interest.
{"title":"Continual learning for fault diagnosis considering variable working conditions","authors":"Dongdong Wei, Ming Jian Zuo, Zhigang Tian","doi":"10.1177/1748006x241252469","DOIUrl":"https://doi.org/10.1177/1748006x241252469","url":null,"abstract":"Traditional Neural Networks (NNs) trained in a one-stage process often struggle to perform well when presented with new classes or domain shifts in testing datasets. In fault diagnosis, it is essential to handle a sequence of diagnostic tasks with new fault classes and working conditions. This paper presents a multi-staged Continual Learning algorithm that learns from a sequence of diagnostic tasks. In each training stage, a small portion of previously seen training data is incorporated to help the model remember old tasks and better learn new tasks. A novel scheme is designed to select previously seen data from multiple old tasks, considering their different working conditions. A multi-way domain adaptation is then conducted to mitigate the impact of multiple changes in working conditions among different tasks. The proposed method is tested using two different experiment test rigs, including both gear and bearing faults. Results demonstrate that the proposed Continual Learning algorithm allows NNs to learn from a sequence of diagnostics tasks efficiently and maintain high accuracies for all the tasks of interest.","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":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141507148","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 : 2024-06-01DOI: 10.1177/1748006x241254603
Akhand Rai, Jong-Myon Kim, Anil Kumar, Palani Selvaraj Balaji
Fault diagnosis of gears plays an important role in reducing downtime and maximizing efficiency of rotating machinery. Vibration is popular parameter for gear fault detection. The occurrence of faults produces recurring transient impulses in the gear vibration signals. However, these transient features are heavily masked by background noises making it difficult to investigate gear faults. Furthermore, the development of automated fault diagnosis techniques requiring minimal human supervision poses another big challenge. Consequently, in this paper, an automated fault diagnosis technique based on a novel information theoretic (IT)-derived-feature set and an artificial intelligence technique called as error-correcting output codes-support vector machine (ECOC-SVM) is proposed. The gear vibration signals are first processed by continuous wavelet transform to obtain the corresponding time-frequency distributions (TFDs). The TFDs of the faulty signals are then discriminated from those of the healthy ones by introducing IT measures, namely, Kulback-Leibller divergence (KLD), Jensen-Shannon divergence (JSD), Jensen-Rényi divergence (JRD), and Jensen-Tsallis divergence (JTD). These uni-dimensional-IT measures are modified to accommodate the bidimensional TFDs, and the resultant features are referred to as bidimensional time-frequency information theoretic divergence (BTF-ITD) features. The BTF-ITD features are then used to train the ECOC-SVM model. Finally, the trained ECOC-SVM model is used for testing the gear faults. The ECOC approach rectifies the biases and errors in SVM model predictions. The experimental results confirm that the proposed approach provides higher classification accuracy than time-domain features; voting-based-multiclass SVM; and deep learning techniques, such as those based on the stacked sparse autoencoder (SSAE), deep neural network (DNN), and convolution neural network (CNN).
{"title":"Gear fault diagnosis based on bidimensional time-frequency information theoretic features and error-correcting output codes: A multi-class support vector machine","authors":"Akhand Rai, Jong-Myon Kim, Anil Kumar, Palani Selvaraj Balaji","doi":"10.1177/1748006x241254603","DOIUrl":"https://doi.org/10.1177/1748006x241254603","url":null,"abstract":"Fault diagnosis of gears plays an important role in reducing downtime and maximizing efficiency of rotating machinery. Vibration is popular parameter for gear fault detection. The occurrence of faults produces recurring transient impulses in the gear vibration signals. However, these transient features are heavily masked by background noises making it difficult to investigate gear faults. Furthermore, the development of automated fault diagnosis techniques requiring minimal human supervision poses another big challenge. Consequently, in this paper, an automated fault diagnosis technique based on a novel information theoretic (IT)-derived-feature set and an artificial intelligence technique called as error-correcting output codes-support vector machine (ECOC-SVM) is proposed. The gear vibration signals are first processed by continuous wavelet transform to obtain the corresponding time-frequency distributions (TFDs). The TFDs of the faulty signals are then discriminated from those of the healthy ones by introducing IT measures, namely, Kulback-Leibller divergence (KLD), Jensen-Shannon divergence (JSD), Jensen-Rényi divergence (JRD), and Jensen-Tsallis divergence (JTD). These uni-dimensional-IT measures are modified to accommodate the bidimensional TFDs, and the resultant features are referred to as bidimensional time-frequency information theoretic divergence (BTF-ITD) features. The BTF-ITD features are then used to train the ECOC-SVM model. Finally, the trained ECOC-SVM model is used for testing the gear faults. The ECOC approach rectifies the biases and errors in SVM model predictions. The experimental results confirm that the proposed approach provides higher classification accuracy than time-domain features; voting-based-multiclass SVM; and deep learning techniques, such as those based on the stacked sparse autoencoder (SSAE), deep neural network (DNN), and convolution neural network (CNN).","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":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189733","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 : 2024-04-13DOI: 10.1177/1748006x241246916
Bruno Castanier, Rasa Remenyte-Prescott, Giovanni Sansavini, Christophe Bérenguer
{"title":"Post-ESREL 2021 special section: Reliability, risk, and resilience engineering for structures and infrastructures","authors":"Bruno Castanier, Rasa Remenyte-Prescott, Giovanni Sansavini, Christophe Bérenguer","doi":"10.1177/1748006x241246916","DOIUrl":"https://doi.org/10.1177/1748006x241246916","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":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140566014","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 : 2024-04-03DOI: 10.1177/1748006x241240815
Wang Xiaoyi, Chang Xinyue, Meng Pengfei, Wang Wenxuan
In this study, the detailed derivation of unconditional and conditional statistical moments for calculating two variance-based global sensitivity indices is presented based on the multiplicative dimensional reduction method. Furthermore, an efficient calculation method for the statistical moment of performance function is proposed using Taylor series expansion, transforming it into a calculation of the statistical moment of random variable. Additionally, a recursive formula for the raw moment of normally distributed random variables is derived and a calculation formula for non-normally distributed random variables’ raw moment is provided. Finally, by combining the multiplicative dimensional reduction method with Taylor series expansion, two more effective methods are proposed for variance-based global sensitivity index. Compared with the reference method, the two proposed methods increase efficiency by 66.66% and 33.33%, respectively. The accuracy and efficiency of this approach are verified by a low-dimensional roof truss and a high-dimensional ten-bar truss structure in conjunction with finite element software. Moreover, its engineering application value is demonstrated by applying it to a high-dimensional complex hydraulic piping system containing 28 input variables.
{"title":"An efficient variance-based global sensitivity analysis method based on multiplicative dimensional reduction method and Taylor series expansion","authors":"Wang Xiaoyi, Chang Xinyue, Meng Pengfei, Wang Wenxuan","doi":"10.1177/1748006x241240815","DOIUrl":"https://doi.org/10.1177/1748006x241240815","url":null,"abstract":"In this study, the detailed derivation of unconditional and conditional statistical moments for calculating two variance-based global sensitivity indices is presented based on the multiplicative dimensional reduction method. Furthermore, an efficient calculation method for the statistical moment of performance function is proposed using Taylor series expansion, transforming it into a calculation of the statistical moment of random variable. Additionally, a recursive formula for the raw moment of normally distributed random variables is derived and a calculation formula for non-normally distributed random variables’ raw moment is provided. Finally, by combining the multiplicative dimensional reduction method with Taylor series expansion, two more effective methods are proposed for variance-based global sensitivity index. Compared with the reference method, the two proposed methods increase efficiency by 66.66% and 33.33%, respectively. The accuracy and efficiency of this approach are verified by a low-dimensional roof truss and a high-dimensional ten-bar truss structure in conjunction with finite element software. Moreover, its engineering application value is demonstrated by applying it to a high-dimensional complex hydraulic piping system containing 28 input variables.","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":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140565946","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}
An efficient maintenance policy allows for determining the current state of a system (diagnosis phase) and its future state (prognosis phase). We show in this paper that Markovian methods allow for obtaining many efficient indicators for the expert. To characterize the quality and robustness of these methods, we compared the Hidden Semi-Markov Model (HSMM) with the Hidden Markov Model (HMM). Several learning and decoding methods were included in the competition. A real case study was used as a particularly interesting working tool. The Remaining Useful Life (RUL) has also been included in this work.
{"title":"HSMM multi-observations for prognostics and health management","authors":"Lestari Handayania, Pascal Vrignat, Frédéric Kratz","doi":"10.1177/1748006x241238582","DOIUrl":"https://doi.org/10.1177/1748006x241238582","url":null,"abstract":"An efficient maintenance policy allows for determining the current state of a system (diagnosis phase) and its future state (prognosis phase). We show in this paper that Markovian methods allow for obtaining many efficient indicators for the expert. To characterize the quality and robustness of these methods, we compared the Hidden Semi-Markov Model (HSMM) with the Hidden Markov Model (HMM). Several learning and decoding methods were included in the competition. A real case study was used as a particularly interesting working tool. The Remaining Useful Life (RUL) has also been included in this work.","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":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140300927","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 : 2024-03-04DOI: 10.1177/1748006x241235727
Rui Zheng, Yanying Song, Haojun Fang
The failure rate of wind turbines shows obvious fluctuation due to seasonal environmental factors. However, few efforts have been devoted to modeling the seasonal failure rate. This paper develops a novel model that consists of a baseline failure rate function, seasonal indices, and a residual term to describe the monthly failure rate of wind turbines. A two-stage procedure is developed to estimate the 16 unknown parameters in the model. The first stage explores the relationship between the parameters in the baseline function and the monthly coefficients by maximum likelihood estimation and then integrates the properties into the genetic algorithm to estimate the main parameters. In the second stage, the variance of the residual term is estimated based on the analysis of the differences between the observed and predicted failure rates. The failure history of 48 months has been used to illustrate the proposed approach. The results show that the monthly failure rate function can well fit the real failure history of wind turbines, and it outperforms the traditional reliability model.
{"title":"Reliability analysis of wind turbines considering seasonal weather effects","authors":"Rui Zheng, Yanying Song, Haojun Fang","doi":"10.1177/1748006x241235727","DOIUrl":"https://doi.org/10.1177/1748006x241235727","url":null,"abstract":"The failure rate of wind turbines shows obvious fluctuation due to seasonal environmental factors. However, few efforts have been devoted to modeling the seasonal failure rate. This paper develops a novel model that consists of a baseline failure rate function, seasonal indices, and a residual term to describe the monthly failure rate of wind turbines. A two-stage procedure is developed to estimate the 16 unknown parameters in the model. The first stage explores the relationship between the parameters in the baseline function and the monthly coefficients by maximum likelihood estimation and then integrates the properties into the genetic algorithm to estimate the main parameters. In the second stage, the variance of the residual term is estimated based on the analysis of the differences between the observed and predicted failure rates. The failure history of 48 months has been used to illustrate the proposed approach. The results show that the monthly failure rate function can well fit the real failure history of wind turbines, and it outperforms the traditional reliability model.","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":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036764","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 : 2024-03-04DOI: 10.1177/1748006x241235979
Monojit Das, V.N.A. Naikan, Subhash Chandra Panja
Predicting the cutting tool life is crucial for effectively managing machining costs, ensuring product quality, maintaining equipment availability and minimising waste in machining processes. When machining heat-resistant superalloys such as Inconel, the concern for tool life becomes even more pronounced. Cutting tool failure is a complex phenomenon that depends on several variables, including tool type and material, workpiece material, machine tool type and machining parameters. Traditional run-to-fail tests to predict tool life are costly and time-consuming. To address these challenges, accelerated degradation testing (ADT) offers a promising solution. ADT involves subjecting the component to higher levels of parameters, causing it to fail faster than under normal conditions. This approach saves time and reduces expenses associated with tool life tests for valuable workpieces. In implementing the concept of ADT, the experimental cutting speed [Formula: see text] values are selected much higher than the normal usage levels in the present study. The tool life tests are performed at three levels of [Formula: see text], feed rate [Formula: see text], depth of cut [Formula: see text] and tool nose radius [Formula: see text] using the Taguchi L9 orthogonal array. Parametric statistical approaches, that is, accelerated failure time (AFT) models, are applied with distributions, namely the Weibull, lognormal and log-logistic distributions, to analyse the cutting tool’s reliability based on predictor variables. Various tool wear modes are considered criteria for tool failure. The comparison is made among the mean time to failure (MTTF) of cutting tools as predicted by various fitted models. Additionally, a favourable tool failure pattern is observed when using the middle level of [Formula: see text] and operating at relatively higher [Formula: see text] values while ensuring that [Formula: see text] and [Formula: see text] values fall within the recommended range. The proposed approach has the potential for diverse applications, including assessing the reliability of cutting tools and tool condition monitoring.
{"title":"Reliability analysis of PVD-coated carbide tools during high-speed machining of Inconel 800","authors":"Monojit Das, V.N.A. Naikan, Subhash Chandra Panja","doi":"10.1177/1748006x241235979","DOIUrl":"https://doi.org/10.1177/1748006x241235979","url":null,"abstract":"Predicting the cutting tool life is crucial for effectively managing machining costs, ensuring product quality, maintaining equipment availability and minimising waste in machining processes. When machining heat-resistant superalloys such as Inconel, the concern for tool life becomes even more pronounced. Cutting tool failure is a complex phenomenon that depends on several variables, including tool type and material, workpiece material, machine tool type and machining parameters. Traditional run-to-fail tests to predict tool life are costly and time-consuming. To address these challenges, accelerated degradation testing (ADT) offers a promising solution. ADT involves subjecting the component to higher levels of parameters, causing it to fail faster than under normal conditions. This approach saves time and reduces expenses associated with tool life tests for valuable workpieces. In implementing the concept of ADT, the experimental cutting speed [Formula: see text] values are selected much higher than the normal usage levels in the present study. The tool life tests are performed at three levels of [Formula: see text], feed rate [Formula: see text], depth of cut [Formula: see text] and tool nose radius [Formula: see text] using the Taguchi L<jats:sub>9</jats:sub> orthogonal array. Parametric statistical approaches, that is, accelerated failure time (AFT) models, are applied with distributions, namely the Weibull, lognormal and log-logistic distributions, to analyse the cutting tool’s reliability based on predictor variables. Various tool wear modes are considered criteria for tool failure. The comparison is made among the mean time to failure (MTTF) of cutting tools as predicted by various fitted models. Additionally, a favourable tool failure pattern is observed when using the middle level of [Formula: see text] and operating at relatively higher [Formula: see text] values while ensuring that [Formula: see text] and [Formula: see text] values fall within the recommended range. The proposed approach has the potential for diverse applications, including assessing the reliability of cutting tools and tool condition monitoring.","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":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025407","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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