Browsing by Author "Fang, Zhigeng"

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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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    Grey relational analysis model with cross-sequences and its application in evaluating air quality index
    (Elsevier, 2023-06-26) Lu, Ningning; Liu, Sifeng; Du, Junliang; Fang, Zhigeng; Dong, Wenjie; Tao, Liangyan; Yang, Yingjie
    It is important to detect the internal operating regularity in system developing with poor information. To identify the real relationship among multi factors, we propose a grey relational analysis (GRA) method inspired by the characteristics of sequences variation. The proposed model considers the changes of fluctuating sequences like cross-sequences both in domain time and between time intervals. To obtain the quantitative change about sequences, relative angle change is employed to determine the variation in each interval, and the relative angle oscillation change is utilized for measuring variations between intervals. To find the optimal time lag or time intervals, the corresponding cycles are extracted by time-delay models. The reliability of the proposed models will be verified through cases in identifying crucial factors for air quality, and the final detection will then be made. To compare with existing representative GRA models clearly, the relation between two fluctuating sequences shaped in cross-sequences is examined by the proposed model. The empirical results show that the relation degree between pollutants and air quality is reasonable. The compared experiment shows that the GRA for cross-sequences can effectively identify the relationship among fluctuating sequences and the impact of time-delay is small for the proposed model with similar shapes.
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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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    On Spectral Analysis and New Research Directions in Grey System Theory
    (Research Information Ltd, 2020-03-15) Liu, Sifeng; Lin, Changhai; Tao, Liangyan; Javed, Saad Ahmed; Fang, Zhigeng; Yang, Yingjie
    Spectral analysis as a powerful mean to identify the characteristics of data series is introduced in this paper. The problems requiring further explorations in grey system theory are also identified. This includes discrimination of various factors of a data sequence in frequency domain, spectral analysis of various sequence operators, the synthesis axiom of degree of greyness for “multiplication” and “division” etc. Further, how to select and test a grey prediction model? How to select and test an inverse grey incidence analysis model? The test rules and criteria of grey clustering evaluation models, etc.
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    Relation between China’s gasoline prices and international crude oil prices
    (Taylor & Francis, 2016-12-07) Yuan, Chaoqing; Yang, Yingjie; Liu, Sifeng; Fang, Zhigeng
    China’s gasoline prices are still regulated by government although China's refined oil pricing mechanism has been reformed for many times. In this paper, proximity and similarity between China’s gasoline prices and Brent crude oil prices are calculated in the different stages with China’s refined oil pricing mechanism reforming, by using daily data and grey relational analysis method. The results show that there have undergone great changes of the similarity and proximity between China’s gasoline prices and international crude oil prices.
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    Research on Index System for Disabled Elders Evaluation and Grey Clustering Model Based on End-point Mixed Possibility Functions
    (Research Information Ltd, 2019-11-01) Liu, Sifeng; Fan, Yun; Yang, Yingjie; Tan, Xuerui; Fang, Zhigeng; Chen, Yequn; Zhang, Qin; Xie, Naiming; Yuan, Chaoqing; Xue, Qingyuan; Tao, Liangyan; Guo, Xiaojun
    An operational ability assessment system for older adults is of great help to address health and social challenges for ageing. In this paper, the main problems in currently available ADL and ability evaluation systems have been analyzed. The basic principles to build an index system for disability elders evaluation have been put forwarded. Then，an improved Barthel index system for ADL evaluation and a new older adults ability evaluation system consisted of 4 first-level indexes and 14 secondary indexes based on experts’ opinion and the ability assessment system for older adults by Ministry of Civil Affairs of China have been built. The grey clustering model based on end-point mixed triangular possibility function has been introduced. And three living examples of adults’ disability evaluation have been conducted. It is confirmed clearly that the three older adults belong to different categories of "severe disability", "mild disability", and "ability passable" respectively. The research results can be used as reference for government to formulate the elderly-care policies, to run and allocate the elderly-care resources, as well as reference for various nursing or elderly-care institutions.
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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.
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    Spectrum analysis of moving average operator and construction of time-frequency hybrid sequence operator
    (Emerald, 2021-02-22) Lin, Changhai; Liu, Sifeng; Fang, Zhigeng; Yang, Yingjie
    Purpose – The purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator. Design/methodology/approach – Firstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise. Findings – Through the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified. Practical implications – The real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data. Originality/value – Firstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.