Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines

Zhang, Hong; Li, Zhuguo; Chen, Zhaoneng

doi:10.1016/s0301-679x(03)00056-2

Cited by 50 publications

(14 citation statements)

References 4 publications

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“…There are many potential applications for grey system models: for example in hydrology [14], in control systems [15], in the analysis of technical systems [16], in the description of the wear-out process [7], in economy [17], in geology [18]. Many studies in these areas are connected with prediction on the basis of small amount of data.…”

Section: Making Forecasts Using Methods Based On Gm(11) Modelsmentioning

confidence: 99%

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Using a set of GM(1,1) models to predict values of diagnostic symptoms

Tabaszewski

Cempel

2015

Mechanical Systems and Signal Processing

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Section: Making Forecasts Using Methods Based On Gm(11) Modelsmentioning

confidence: 99%

“…Of the many different prognostic methods (e.g. [4,5]), one can use grey system theory and its simplest model GM(1,1) [6][7][8][9][10][11], particularly when we have small amount of data, i.e. few measurements of symptom values only.…”

Section: Introductionmentioning

confidence: 99%

Using a set of GM(1,1) models to predict values of diagnostic symptoms

Tabaszewski

Cempel

2015

Mechanical Systems and Signal Processing

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“…The Grey model (GM) (1,1), that is, a single variable fi rst-order Grey model, is the most important prediction model. A condition to establish a GM(1,1) is that the data should be taken at equal intervals ( Zhang et al ., 2003 ). Grey systems modelling is carried out mainly through the generation of Grey numbers or functions of series operators to fi nd the hidden patterns.…”

Section: Methodsmentioning

confidence: 99%

Forecasting and Evaluating the Tourist Hotel Industry Performance in Taiwan Based on Grey Theory

Wu¹,

Hsiao²,

Tsai³

2008

Tourism and Hospitality Research

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Although Grey theory is extensively adopted to construct the forecasting models for evaluating organisational performance, it is rarely used in the hotel industry. This study draws on the GM(1,1) model to accurately predict the coming year's output value and on Grey relational analysis to select the best-performing hotels in Taiwan. Measures of hotel performance — including profit before tax, ROI before tax, revenue per employee, REVPAR, revenue per square meter, and occupancy rate — were collected from the Tourism Bureau in Taiwan. These data contain the performance of 56 international tourist hotels in 2002 and industry data from 1992 to 2005. Results in this study indicate that four-point GM(1,1) is the best model for predicting output value in the future. In addition, this investigation reveals the various competitive advantages and strategies in these top hotels, such as their appropriate site, higher price, and higher occupancy rate.

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“…It explores system behavior using relation analysis and model construction (Liu and Lin 41 ). Grey system theory has been widely used in many fields such as (i) forecasting (Hsu and Chen 42 , Zhang et al 43 ), (ii) industrial applications (Lin and Yang 44 ) and (iii) engineering applications (Wang et al 45 , Lian et al 46 ). The use of grey theory within the FMEA framework was accomplished by Chang et al 47 .…”

Section: Basic Notions On the Grey Approachmentioning

confidence: 99%

Modeling system behavior for risk and reliability analysis using KBARM

Sharma

Kumar

2007

Quality & Reliability Eng

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With advances in technology and the growing complexity of technological systems, the job of the reliability/system analyst has become more challenging as they have to study, characterize, measure and analyze the behavior of systems with the help of various traditional analytical (mathematical and statistical) techniques, which require knowledge of the precise numerical probabilities and component functional dependencies, information which is difficult to obtain. Even if data are available they are often inaccurate and are thus subject to uncertainty, i.e. historical records can only represent the past behavior and may be unable to predict the future behavior of the equipment. To cope with such situations, the knowledge-based approximate reasoning methodologies (KBARMs) provide necessary help. Among them, the fuzzy and grey methodologies are the most viable and effective tools for coping with imprecise, uncertain and subjective information in a consistent and logical manner. In this paper, the authors present a methodological and structured approach (which makes use of both qualitative and quantitative techniques) to model, analyze and predict the failure behavior of two units, namely the forming and press units of a paper machine, using KBARMs. Various system parameters such as repair time, failure rate, mean time between failures, availability and expected number of failures are computed to quantify the system behavior in terms of fuzzy, crisp and defuzzified values. Furthermore, a risk ranking approach based on fuzzy and grey relational analysis is discussed to prioritize various failure causes associated with the components in failure mode and effects analysis (FMEA).. He has 26 years of research/teaching and industrial experience and has written over 130 papers that haven been published in international and national journals and conferences. He has guided a number of students for their undergraduate projects, masters dissertations and PhD degrees. His fields of interests are system behavior in industry, maintenance engineering and reliability analysis.Pradeep Kumar has more than 21 years of experience in teaching, research, consultancy and industry at IIT Roorkee, NIT, Kurukshetra, West Virginia University, Wayne State University, SAIL and DRF Industries. He has taught a wide spectrum of courses related to industrial engineering and production engineering. He has guided 10 PhD theses with five more PhD theses in progress, 45 MTech dissertations, 30 MTech projects and 40 BTech projects. He has published/presented more than 226 research papers in international and national journals and in the proceedings of international and national conferences. He has handled eight major consultancy projects for various organizations and six sponsored research projects in India. His research interests include industrial engineering, quality engineering, robust design methodologies, reliability engineering, project management, production and operations management, Total Quality Management (TQM), supply chain management, multic...

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Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines

Cited by 50 publications

References 4 publications

Using a set of GM(1,1) models to predict values of diagnostic symptoms

Using a set of GM(1,1) models to predict values of diagnostic symptoms

Forecasting and Evaluating the Tourist Hotel Industry Performance in Taiwan Based on Grey Theory

Modeling system behavior for risk and reliability analysis using KBARM

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