Browsing by Author "Yang, Xiaoyu"

Now showing 1 - 11 of 11
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    Canonical variate residuals-based contribution map for slowly evolving faults
    (Elsevier, 2019-02-23) Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Bennett, Ian; Collop, Andy; Mba, David
    The superior performance of canonical variate analysis (CVA) for fault detection has been demonstrated by a number of researchers using simulated and real industrial data. However, applications of CVA to fault identification of industrial processes, especially for faults that evolve slowly, are not widely reported. In order to improve the performance of traditional CVA-based methods to slowly developing faults, a novel diagnostic approach is put forward to implement incipient fault diagnosis for dynamic process monitoring. Traditional CVA fault detection approach is extended to form a new monitoring index based on indices, Hotelling’s T2, Q and a canonical variate residuals (CVR)-based monitoring index Td. As an alternative to the traditional CVA-based contributions, a CVR-based contribution plot method is proposed based on Q and Td statistics. The proposed method is shown to facilitate fault detection by increasing the sensitivity to incipient faults, and aid fault identification by enhancing the contributions from fault- related variables and suppressing the contributions from fault-free variables. The CVR-based method has been demonstrated to outperform traditional CVA-based diagnostic methods for fault detection and identification when validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system and an industrial centrifugal pump.
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    A components-based software framework for product lifecycle information management for consumer products.
    (IEEE, 2007-08-01) Yang, Xiaoyu; Moore, Philip R.; Wong, Chi Biu; Pu, Junsheng
    In this paper, a component-based software framework that can facilitate the development of lifecycle information management systems for consumer products is developed. This software framework can accommodate the management of lifecycle data at all stages, especially data that occur in distribution, usage, maintenance and end-of life stages, and use them to provide information and knowledge. The software framework is built with software components, based on a component-based concept with reference to a product lifecycle information acquisition and management model proposed within this research study. Two actual lifecycle information management systems are created using the Framework in field trials. This software framework can open new horizons for product design which are sustainable and environmentally sensitive. It also contributes to the wider exploration of middleware solutions for implementing next generation consumer products (e.g. smart home appliances) and intelligent products.
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    Grey GERT Network Model of Equipment Lifetime Evaluation Based on Small Samples
    (Research Information Ltd., 2018-01) Yang, Xiaoyu; Fang, Zhigeng; Tao, L.
    The reliability evaluation of high reliability and long life equipment is widely concerned in recent decades. Enough failure samples of these kinds of equipment are not easy or economic to obtain in reliability test, in addition, experience information is sometimes inaccurate or uncertainty. To overcome the deficiency in traditional method which requires large numbers of samples, a quantitative analysis model of equipment reliability evaluation is proposed in this paper in view of the few failure data of equipment life tests. GERT network is introduced to describe the kinds of working states of the equipment system and random process of equipment state transition choice after stress impact of single component. Considering the uncertainty and inaccuracy of the statistical data and experience information, the parameters of GERT network are represented by interval grey number. The system equivalent transfer function could be obtained by GERT matrix solving algorithm, and the reliability evaluation of equipment system can be realized. The case study results show that the equipment reliability evaluation Grey-GERT model based on small samples would save much time with little accuracy losing. Besides, the study provides a new thinking for reliability accelerated life test.
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    An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data
    (Sage, 2019) Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Bennett, Ian; Mba, David
    In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring.
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    Intelligent products: from lifecycle data acquisition to enabling product-related services.
    (Elsevier, 2009) Moore, Philip R.; Chong, Seng Kwong; Yang, Xiaoyu
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    A Novel Diagnostic and Prognostic Framework for Incipient Fault Detection and Remaining Service Life Prediction with Application to Industrial Rotating Machines
    (Elsveier, 2019-06-21) Li, Xiaochuan; Yang, Xiaoyu; Yang, Yingjie; Ian, Bennett; Mba, David
    Data-driven machine health monitoring systems (MHMS) have been widely investigated and applied in the field of machine diagnostics and prognostics with the aim of realizing predictive maintenance. It involves using data to identify early warnings that indicate potential system malfunctioning, predict when system failure might occur, and pre-emptively service equipment to avoid unscheduled downtime. One of the most critical aspects of data-driven MHMS is the provision of incipient fault diagnosis and prognosis regarding the system’s future working conditions. In this work, a novel diagnostic and prognostic framework is proposed to detect incipient faults and estimate remaining service life (RSL) of rotating machinery. In the proposed framework, a novel canonical variate analysis (CVA)-based monitoring index, which takes into account the distinctions between past and future canonical variables, is employed for carrying out incipient fault diagnosis. By incorporating the exponentially weighted moving average (EWMA) technique, a novel fault identification approach based on Pearson correlation analysis is presented and utilized to identify the influential variables that are most likely associated with the fault. Moreover, an enhanced metabolism grey forecasting model (MGFM) approach is developed for RSL prediction. Particle filter (PF) is employed to modify the traditional grey forecasting model for improving its prediction performance. The enhanced MGFM approach is designed to address two generic issues namely dealing with scarce data and quantifying the uncertainty of RSL in a probabilistic form, which are often encountered in the prognostics of safety-critical and complex assets. The proposed CVA-based index is validated on slowly evolving faults in a continuous stirred tank reactor (CSTR) system, and the effectiveness of the proposed integrated diagnostic and prognostic method for the monitoring of rotating machinery is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump and one case study of an operational centrifugal compressor.
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    A novel multi-information fusion grey model and its application in wear trend prediction of wind turbines
    (Elsevier, 2019-03-05) Yang, Xiaoyu; Fang, Zhigeng; Yang, Yingjie; Mba, David; Li, Xiaochuan
    The small and fluctuating samples of lubricating oil data render the wear trend prediction a challenging task in operation and maintenance management of wind turbine gearboxes. To deal with this problem, this paper puts forward a method to enhance the prediction accuracy and robustness of the grey prediction model by introducing multi-source information into traditional grey models. Multi-source information is applied by creating a mapping sequence according to the sequence to be predicted. The significance of the key parameters in the proposed model was investigated by numerical experiments. Based on the results from the numerical experiments, the effectiveness of the proposed method was demonstrated using lubricating oil data captured from industrial wind turbine gearboxes. A comparative analysis was also conducted with a number of selected other models to illustrate the superiority of the proposed model in dealing with small and fluctuating data. Prediction results show that the proposed model is able to relax the quasi-smooth requirement of data sequence and is much more robust in comparison to exponential regression, linear regression and non-equidistance GM(1,1) models.
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    A practical methodology for realizing product service systems for consumer products.
    (Elsevier, 2009) Yang, Xiaoyu; Moore, Philip R.; Pu, Junsheng; Wong, Chi Biu
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    Product life cycle information acquisition and management for consumer products
    (Emerald, 2007-01-01) Yang, Xiaoyu; Moore, Philip R.; Wong, Chi Biu; Pu, Junsheng; Chong, Seng Kwong
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    Similarity-based information fusion grey model for remaining useful life prediction of aircraft engines
    (Emerald, 2021-06-18) Yang, Xiaoyu; Fang, Zhigeng; Li, Xiaochuan; Yang, Yingjie; Mba, David
    Purpose Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines. Design/methodology/approach First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly. Findings The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce. Research limitations/implications The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine. Practical implications The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps. Originality/value This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.