Quality Engineering最新文献_第8页

Reliability modeling for failure correlations to a CNC lathe subsystem based on Pagerank Algorithm 基于Pagerank算法的数控车床子系统故障关联可靠性建模

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-05-08 DOI: 10.1080/08982112.2023.2208664

Dongwei Gu, Jin Guo, Wenbo Han, Song Gao, Xilu Zhao, Zhen Xu

引用次数: 0

Tukey-EWMA control chart with variable sampling intervals for process monitoring 过程监控中可变采样间隔的Tukey-EWMA控制图

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-04-24 DOI: 10.1080/08982112.2023.2199824

Selcem Adsız, Burcu Aytaçoğlu

引用次数: 0

Step-stress tests for interval-censored data under gamma lifetime distribution γ寿命分布下区间截尾数据的阶跃应力测试

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-04-21 DOI: 10.1080/08982112.2023.2199826

N. Balakrishnan, María Jaenada, L. Pardo

引用次数: 1

I-optimal or G-optimal: Do we have to choose? i最优还是g最优，我们必须选一个吗?

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-04-13 DOI: 10.1080/08982112.2023.2194963

Stephen J. Walsh, Lu Lu, C. Anderson‐Cook

引用次数: 0

Mutual combination of selected principles and technologies of Industry 4.0 and quality management methods - case study 工业4.0的选定原则和技术与质量管理方法的相互结合——案例研究

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-04-13 DOI: 10.1080/08982112.2023.2193895

Pavel Klaput, R. Hercík, Z. Machacek, Darja Noskievičová, V. Dostál, D. Vykydal

引用次数: 0

Process Analytical Technology, continuous manufacturing, and the development of a surrogate model for dissolution: a pharmaceutical manufacturing statistical engineering case study 过程分析技术、连续制造和溶出度替代模型的发展:一个制药制造统计工程案例研究

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-04-06 DOI: 10.1080/08982112.2023.2196556

S. Altan, H. Coppenolle, L. Hare, Sarah Nielsen, Martin Otava

{"title":"Process Analytical Technology, continuous manufacturing, and the development of a surrogate model for dissolution: a pharmaceutical manufacturing statistical engineering case study","authors":"S. Altan, H. Coppenolle, L. Hare, Sarah Nielsen, Martin Otava","doi":"10.1080/08982112.2023.2196556","DOIUrl":"https://doi.org/10.1080/08982112.2023.2196556","url":null,"abstract":"Abstract Statistical engineering is a recent movement that seeks to promote a systematic application of statistical tools combined with a comprehensive view of project management to solve important commercial problems. We discuss how statistical engineering principles were applied in the development of a surrogate model for dissolution (also referred to as in vitro release), an important physical property of solid dose pharmaceutical products required for marketing. The approach relies on spectroscopic analytical methods, referred to more generally as Process Analytic Technology, to enable monitoring of a batch of pharmaceutical product in real time. Real time release (RTR) is the release to the market of a batch of final product, nearly immediately after its production, rather than the weeks to months currently needed using conventional wet chemistry analytical methods. The development of a surrogate model advances the goal of RTR of a batch of pharmaceutical product, and the application of statistical engineering tools enhances a coherent development process as illustrated in a case study.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46456013","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}

引用次数: 0

Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals 基于振动监测信号的强化学习优化支持向量机模型早期故障诊断

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-04-03 DOI: 10.1080/08982112.2023.2193255

Wenqin Zhao, Yaqiong Lv, Jialun Liu, C. Lee, Lei Tu

{"title":"Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals","authors":"Wenqin Zhao, Yaqiong Lv, Jialun Liu, C. Lee, Lei Tu","doi":"10.1080/08982112.2023.2193255","DOIUrl":"https://doi.org/10.1080/08982112.2023.2193255","url":null,"abstract":"Abstract Effective fault diagnosis maximizes economic benefits by ensuring the stability of machinery systems. Detecting the faults of the key components in machinery, such as rolling bearings, at an early stage, helps to avoid accidents to optimize the maintenance efficiency. It is well known that faulty bearings always deliver a message through their abnormal vibration variation, which can be captured by vibration acceleration sensors in order to facilitate the deteriorating status assessment. However, a clue for an early fault is so ambiguous and sometimes masked by ambient noise, which makes the early fault diagnosis a challenging problem. To tackle the problem, we propose a vibration signal-based data-driven early fault diagnosis approach based on the reinforcement learning (RL) optimized support vector machine (SVM) model. The exploration of the hyperparameter optimization using RL to improve SVM performance motivates this research. Firstly, the corresponding features in the time domain, frequency domain and time-frequency domain are extracted from the obtained vibration signals of the key components under certain working conditions. Subsequently, to better recognize the pattern of an early fault, linear discriminant analysis (LDA) is employed in fuzing the multi-domain early fault features. Finally, the fused features are fed into the RL optimized-SVM model for fault diagnosis. Experimental validation was performed with a public dataset of rolling bearings, and the results confirmed the effectiveness and superiority of the approach compared with other methods.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"59458450","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}

引用次数: 2

On efficient change point detection using a step cumulative sum control chart 基于阶跃累积和控制图的有效变化点检测

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-03-31 DOI: 10.1080/08982112.2023.2193896

Nasir Abbas

{"title":"On efficient change point detection using a step cumulative sum control chart","authors":"Nasir Abbas","doi":"10.1080/08982112.2023.2193896","DOIUrl":"https://doi.org/10.1080/08982112.2023.2193896","url":null,"abstract":"Abstract Control charts are widely used to monitor the stability of processes and Shewhart is the most commonly used type of chart because of the simplicity of its structure. To improve the performance, there are several modifications on control chart suggested since its proposal like cumulative sum and exponentially weighted moving average control charts. The cumulative sum chart accumulates all the deviations from target mean higher than a specific design parameter which can be used to controls the sensitivity of this chart. The optimal choice of this design parameter is half of the target shift size. This results into an optimal performance in terms of quick detection of shift that is equal to the target shift size. For shift sizes other than target, the cumulative sum chart loses its performance and becomes inferior to other control charts in terms of average run length. Since we rarely know the process shift size that is going to occur in future, this study proposes a revision in the structure of cumulative sum chart. The adjusted cumulative sum chart is shown to be very flexible for a wide range of shifts, when designed against a target shift size. The proposed adjusted cumulative sum control chart is compared with cumulative sum, exponentially weighted, combined Shewhart-CUSUM, progressive mean, double progressive mean and mixed EWMA-CUSUM control charts in terms of zero- and steady-state average run lengths. The superiority zones are identified and the results are supported by a real-life implementation of the proposed scheme.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41751155","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}

引用次数: 2

Contextual anomaly detection for multivariate time series data 多元时间序列数据的上下文异常检测

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-02-28 DOI: 10.1080/08982112.2023.2179404

Hyojoon Kim, Heeyoung Kim

{"title":"Contextual anomaly detection for multivariate time series data","authors":"Hyojoon Kim, Heeyoung Kim","doi":"10.1080/08982112.2023.2179404","DOIUrl":"https://doi.org/10.1080/08982112.2023.2179404","url":null,"abstract":"Abstract With the advancement of sensing technologies, sensor data collected over time have become more useful for detecting anomalies in underlying processes and systems. Sensor data are often affected by contextual variables, such as equipment settings, and can have different patterns, even in normal states depending on the contextual variables. Motivated by this problem, we propose a contextual anomaly detection method for multivariate time series data. We first build a prediction model using training data consisting of only normal observations, and then perform anomaly detection based on the prediction errors for future observations. The prediction model is based on a long short-term memory (LSTM) network that can flexibly model complex relationships between variables as well as temporal correlations between successive time points using the high expressive power of deep recurrent neural networks. In particular, to incorporate the contextual information while ensuring that it does not propagate over time but affects the response data only at specific target time points, we extend the standard LSTM by adding a layer for the contextual variables separately for each time step. The performance of the proposed method was verified with several open-source datasets and a real dataset from a global tire company.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48897207","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}

引用次数: 0

Remembering George EP Box in Quality Quandaries: great ideas, practical advice, clearly presented 怀念乔治的EP盒子：伟大的想法，实用的建议，清晰的呈现

IF 2 4区工程技术

Quality Engineering Pub Date : 2023-02-21 DOI: 10.1080/08982112.2023.2179924

Pere Grima, L. Marco-Almagro, X. Tort-Martorell

引用次数: 0